Lineage expression over time
#Set working directory to appropriate folder for inputs and outputs on Google Drive
#Initialize
Load data
load('2022_01_14_analysis_scripts/2022_05_27_analysis/Assign_dominant_barcodes/all_data_final_lineages.RData')
load('2022_01_14_analysis_scripts/2022_05_27_analysis/Preprocess_GEX/second_timepoint_merged.RData')
load('2022_01_14_analysis_scripts/2022_05_27_analysis/Filtering_cDNA/resistant_lineage_lists.RData')
load('2022_01_14_analysis_scripts/2022_05_27_analysis/Assign_dominant_barcodes/cis_final_lineages.RData')
load('2022_01_14_analysis_scripts/2022_05_27_analysis/Assign_dominant_barcodes/cocl2_final_lineages.RData')
load('2022_01_14_analysis_scripts/2022_05_27_analysis/Assign_dominant_barcodes/dabtram_final_lineages.RData')
load('2022_01_14_analysis_scripts/2022_05_27_analysis/Assign_dominant_barcodes/dabtram_both_times_final_lineages.RData')
#Plot cell cycle
load('2022_01_14_analysis_scripts/2022_05_27_analysis/Assign_dominant_barcodes/all_data_final_lineages.RData')
load('2022_01_14_analysis_scripts/2022_05_27_analysis/Preprocess_GEX/second_timepoint_merged.RData')
load('2022_01_14_analysis_scripts/2022_05_27_analysis/Filtering_cDNA/resistant_lineage_lists.RData')
load('2022_01_14_analysis_scripts/2022_05_27_analysis/Assign_dominant_barcodes/cis_final_lineages.RData')
load('2022_01_14_analysis_scripts/2022_05_27_analysis/Assign_dominant_barcodes/cocl2_final_lineages.RData')
load('2022_01_14_analysis_scripts/2022_05_27_analysis/Assign_dominant_barcodes/dabtram_final_lineages.RData')
load('2022_01_14_analysis_scripts/2022_05_27_analysis/Assign_dominant_barcodes/dabtram_both_times_final_lineages.RData')
Markers within each first drug object to find subgroups (ie analogous to NGFR/EGFR)
need to make Idents metadata object which says if the cells are included in the combined lins list, or if they were filtered
#load('2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/all_data_markers.RData')
cis_markers <- FindAllMarkers(cis, only.pos = TRUE, min.pct = 0.25, logfc.threshold = 0.25)Calculating cluster 0
| | 0 % ~calculating
|+ | 1 % ~05s
|++ | 2 % ~04s
|++ | 3 % ~04s
|+++ | 4 % ~03s
|+++ | 6 % ~03s
|++++ | 7 % ~03s
|++++ | 8 % ~03s
|+++++ | 9 % ~03s
|+++++ | 10% ~03s
|++++++ | 11% ~03s
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|++++++++ | 14% ~03s
|++++++++ | 16% ~03s
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|++++++++++++++++ | 31% ~02s
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|+++++++++++++++++ | 33% ~02s
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|++++++++++++++++++++ | 39% ~02s
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|++++++++++++++++++++++++ | 47% ~02s
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|++++++++++++++++++++++++++++++ | 60% ~01s
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|++++++++++++++++++++++++++++++++ | 62% ~01s
|++++++++++++++++++++++++++++++++ | 63% ~01s
|+++++++++++++++++++++++++++++++++ | 64% ~01s
|+++++++++++++++++++++++++++++++++ | 66% ~01s
|++++++++++++++++++++++++++++++++++ | 67% ~01s
|++++++++++++++++++++++++++++++++++ | 68% ~01s
|+++++++++++++++++++++++++++++++++++ | 69% ~01s
|+++++++++++++++++++++++++++++++++++ | 70% ~01s
|++++++++++++++++++++++++++++++++++++ | 71% ~01s
|+++++++++++++++++++++++++++++++++++++ | 72% ~01s
|+++++++++++++++++++++++++++++++++++++ | 73% ~01s
|++++++++++++++++++++++++++++++++++++++ | 74% ~01s
|++++++++++++++++++++++++++++++++++++++ | 76% ~01s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~01s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~01s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~01s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~01s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 84% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=03s Calculating cluster 1
| | 0 % ~calculating
|++ | 3 % ~00s
|++++ | 7 % ~00s
|++++++ | 10% ~00s
|+++++++ | 14% ~00s
|+++++++++ | 17% ~00s
|+++++++++++ | 21% ~00s
|+++++++++++++ | 24% ~00s
|++++++++++++++ | 28% ~00s
|++++++++++++++++ | 31% ~00s
|++++++++++++++++++ | 34% ~00s
|+++++++++++++++++++ | 38% ~00s
|+++++++++++++++++++++ | 41% ~00s
|+++++++++++++++++++++++ | 45% ~00s
|+++++++++++++++++++++++++ | 48% ~00s
|++++++++++++++++++++++++++ | 52% ~00s
|++++++++++++++++++++++++++++ | 55% ~00s
|++++++++++++++++++++++++++++++ | 59% ~00s
|++++++++++++++++++++++++++++++++ | 62% ~00s
|+++++++++++++++++++++++++++++++++ | 66% ~00s
|+++++++++++++++++++++++++++++++++++ | 69% ~00s
|+++++++++++++++++++++++++++++++++++++ | 72% ~00s
|++++++++++++++++++++++++++++++++++++++ | 76% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 2
| | 0 % ~calculating
|+ | 1 % ~05s
|++ | 2 % ~04s
|++ | 4 % ~04s
|+++ | 5 % ~04s
|++++ | 6 % ~04s
|++++ | 7 % ~04s
|+++++ | 8 % ~04s
|+++++ | 10% ~04s
|++++++ | 11% ~04s
|+++++++ | 12% ~04s
|+++++++ | 13% ~04s
|++++++++ | 14% ~04s
|++++++++ | 16% ~04s
|+++++++++ | 17% ~04s
|++++++++++ | 18% ~03s
|++++++++++ | 19% ~03s
|+++++++++++ | 20% ~03s
|+++++++++++ | 22% ~03s
|++++++++++++ | 23% ~03s
|+++++++++++++ | 24% ~03s
|+++++++++++++ | 25% ~03s
|++++++++++++++ | 27% ~03s
|++++++++++++++ | 28% ~06s
|+++++++++++++++ | 29% ~05s
|++++++++++++++++ | 30% ~05s
|++++++++++++++++ | 31% ~05s
|+++++++++++++++++ | 33% ~05s
|+++++++++++++++++ | 34% ~05s
|++++++++++++++++++ | 35% ~05s
|+++++++++++++++++++ | 36% ~04s
|+++++++++++++++++++ | 37% ~04s
|++++++++++++++++++++ | 39% ~04s
|++++++++++++++++++++ | 40% ~04s
|+++++++++++++++++++++ | 41% ~04s
|++++++++++++++++++++++ | 42% ~04s
|++++++++++++++++++++++ | 43% ~04s
|+++++++++++++++++++++++ | 45% ~04s
|+++++++++++++++++++++++ | 46% ~03s
|++++++++++++++++++++++++ | 47% ~03s
|+++++++++++++++++++++++++ | 48% ~03s
|+++++++++++++++++++++++++ | 49% ~03s
|++++++++++++++++++++++++++ | 51% ~03s
|++++++++++++++++++++++++++ | 52% ~03s
|+++++++++++++++++++++++++++ | 53% ~03s
|++++++++++++++++++++++++++++ | 54% ~03s
|++++++++++++++++++++++++++++ | 55% ~03s
|+++++++++++++++++++++++++++++ | 57% ~03s
|+++++++++++++++++++++++++++++ | 58% ~02s
|++++++++++++++++++++++++++++++ | 59% ~02s
|+++++++++++++++++++++++++++++++ | 60% ~02s
|+++++++++++++++++++++++++++++++ | 61% ~02s
|++++++++++++++++++++++++++++++++ | 63% ~02s
|++++++++++++++++++++++++++++++++ | 64% ~02s
|+++++++++++++++++++++++++++++++++ | 65% ~02s
|++++++++++++++++++++++++++++++++++ | 66% ~02s
|++++++++++++++++++++++++++++++++++ | 67% ~02s
|+++++++++++++++++++++++++++++++++++ | 69% ~02s
|+++++++++++++++++++++++++++++++++++ | 70% ~02s
|++++++++++++++++++++++++++++++++++++ | 71% ~02s
|+++++++++++++++++++++++++++++++++++++ | 72% ~02s
|+++++++++++++++++++++++++++++++++++++ | 73% ~02s
|++++++++++++++++++++++++++++++++++++++ | 75% ~02s
|++++++++++++++++++++++++++++++++++++++ | 76% ~02s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~02s
|++++++++++++++++++++++++++++++++++++++++ | 78% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~01s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~01s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~01s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 84% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 90% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=06s Calculating cluster 3
| | 0 % ~calculating
|+ | 1 % ~02s
|++ | 3 % ~03s
|++ | 4 % ~02s
|+++ | 5 % ~02s
|++++ | 7 % ~02s
|++++ | 8 % ~02s
|+++++ | 9 % ~02s
|++++++ | 11% ~02s
|++++++ | 12% ~02s
|+++++++ | 13% ~02s
|++++++++ | 14% ~02s
|++++++++ | 16% ~02s
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|+++++++++++++++++ | 33% ~02s
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|++++++++++++++++++ | 36% ~02s
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|++++++++++++++++++++ | 38% ~02s
|++++++++++++++++++++ | 39% ~02s
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|+++++++++++++++++++++++ | 45% ~02s
|++++++++++++++++++++++++ | 46% ~01s
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|+++++++++++++++++++++++++++++++++++++++ | 76% ~01s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~01s
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|+++++++++++++++++++++++++++++++++++++++++ | 82% ~01s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 84% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=03s Calculating cluster 4
| | 0 % ~calculating
|+ | 1 % ~04s
|++ | 2 % ~04s
|++ | 3 % ~04s
|+++ | 5 % ~04s
|+++ | 6 % ~04s
|++++ | 7 % ~03s
|+++++ | 8 % ~03s
|+++++ | 9 % ~03s
|++++++ | 10% ~03s
|++++++ | 12% ~03s
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|++++++++ | 15% ~03s
|+++++++++ | 16% ~03s
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|+++++++++++++ | 26% ~03s
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|+++++++++++++++ | 29% ~03s
|++++++++++++++++ | 30% ~03s
|++++++++++++++++ | 31% ~03s
|+++++++++++++++++ | 33% ~03s
|+++++++++++++++++ | 34% ~02s
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|++++++++++++++++++++ | 38% ~02s
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|++++++++++++++++++++++++ | 47% ~02s
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|++++++++++++++++++++++++++++++ | 59% ~02s
|+++++++++++++++++++++++++++++++ | 60% ~01s
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|++++++++++++++++++++++++++++++++++++++++ | 79% ~01s
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|++++++++++++++++++++++++++++++++++++++++++ | 84% ~01s
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|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
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|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=05s Calculating cluster 5
| | 0 % ~calculating
|+ | 1 % ~07s
|++ | 2 % ~06s
|++ | 3 % ~06s
|+++ | 5 % ~06s
|+++ | 6 % ~06s
|++++ | 7 % ~06s
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|++++++++ | 15% ~06s
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|++++++++++++++++ | 30% ~05s
|++++++++++++++++ | 31% ~04s
|+++++++++++++++++ | 33% ~04s
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|++++++++++++++++++++++++ | 47% ~04s
|++++++++++++++++++++++++ | 48% ~04s
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|+++++++++++++++++++++++++ | 50% ~03s
|++++++++++++++++++++++++++ | 51% ~03s
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|+++++++++++++++++++++++++++++++ | 62% ~03s
|++++++++++++++++++++++++++++++++ | 63% ~03s
|++++++++++++++++++++++++++++++++ | 64% ~02s
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|+++++++++++++++++++++++++++++++++++ | 69% ~02s
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|+++++++++++++++++++++++++++++++++++++ | 72% ~02s
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|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
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|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=07s Calculating cluster 6
| | 0 % ~calculating
|+ | 1 % ~03s
|++ | 2 % ~03s
|++ | 3 % ~03s
|+++ | 5 % ~03s
|+++ | 6 % ~03s
|++++ | 7 % ~03s
|++++ | 8 % ~02s
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|++++++++ | 15% ~02s
|++++++++ | 16% ~02s
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|++++++++++++++++++++++++++++++++ | 64% ~01s
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|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
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|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
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|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=03s Calculating cluster 7
| | 0 % ~calculating
|+ | 1 % ~02s
|++ | 2 % ~02s
|++ | 3 % ~02s
|+++ | 4 % ~02s
|+++ | 5 % ~02s
|++++ | 7 % ~02s
|++++ | 8 % ~02s
|+++++ | 9 % ~02s
|+++++ | 10% ~02s
|++++++ | 11% ~02s
|+++++++ | 12% ~02s
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|++++++++ | 14% ~02s
|++++++++ | 15% ~02s
|+++++++++ | 16% ~02s
|+++++++++ | 18% ~02s
|++++++++++ | 19% ~02s
|++++++++++ | 20% ~02s
|+++++++++++ | 21% ~02s
|+++++++++++ | 22% ~02s
|++++++++++++ | 23% ~02s
|+++++++++++++ | 24% ~02s
|+++++++++++++ | 25% ~02s
|++++++++++++++ | 26% ~02s
|++++++++++++++ | 27% ~02s
|+++++++++++++++ | 29% ~02s
|+++++++++++++++ | 30% ~02s
|++++++++++++++++ | 31% ~02s
|++++++++++++++++ | 32% ~02s
|+++++++++++++++++ | 33% ~02s
|++++++++++++++++++ | 34% ~02s
|++++++++++++++++++ | 35% ~02s
|+++++++++++++++++++ | 36% ~02s
|+++++++++++++++++++ | 37% ~02s
|++++++++++++++++++++ | 38% ~02s
|++++++++++++++++++++ | 40% ~02s
|+++++++++++++++++++++ | 41% ~02s
|+++++++++++++++++++++ | 42% ~02s
|++++++++++++++++++++++ | 43% ~02s
|++++++++++++++++++++++ | 44% ~02s
|+++++++++++++++++++++++ | 45% ~02s
|++++++++++++++++++++++++ | 46% ~01s
|++++++++++++++++++++++++ | 47% ~01s
|+++++++++++++++++++++++++ | 48% ~01s
|+++++++++++++++++++++++++ | 49% ~01s
|++++++++++++++++++++++++++ | 51% ~01s
|++++++++++++++++++++++++++ | 52% ~01s
|+++++++++++++++++++++++++++ | 53% ~01s
|+++++++++++++++++++++++++++ | 54% ~01s
|++++++++++++++++++++++++++++ | 55% ~01s
|+++++++++++++++++++++++++++++ | 56% ~01s
|+++++++++++++++++++++++++++++ | 57% ~01s
|++++++++++++++++++++++++++++++ | 58% ~01s
|++++++++++++++++++++++++++++++ | 59% ~01s
|+++++++++++++++++++++++++++++++ | 60% ~01s
|+++++++++++++++++++++++++++++++ | 62% ~01s
|++++++++++++++++++++++++++++++++ | 63% ~01s
|++++++++++++++++++++++++++++++++ | 64% ~01s
|+++++++++++++++++++++++++++++++++ | 65% ~01s
|+++++++++++++++++++++++++++++++++ | 66% ~01s
|++++++++++++++++++++++++++++++++++ | 67% ~01s
|+++++++++++++++++++++++++++++++++++ | 68% ~01s
|+++++++++++++++++++++++++++++++++++ | 69% ~01s
|++++++++++++++++++++++++++++++++++++ | 70% ~01s
|++++++++++++++++++++++++++++++++++++ | 71% ~01s
|+++++++++++++++++++++++++++++++++++++ | 73% ~01s
|+++++++++++++++++++++++++++++++++++++ | 74% ~01s
|++++++++++++++++++++++++++++++++++++++ | 75% ~01s
|++++++++++++++++++++++++++++++++++++++ | 76% ~01s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 78% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~01s
|+++++++++++++++++++++++++++++++++++++++++ | 80% ~01s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~01s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=03s Calculating cluster 8
| | 0 % ~calculating
|+ | 1 % ~05s
|++ | 2 % ~05s
|++ | 3 % ~05s
|+++ | 4 % ~05s
|+++ | 5 % ~05s
|++++ | 6 % ~05s
|++++ | 7 % ~05s
|+++++ | 8 % ~05s
|+++++ | 9 % ~05s
|++++++ | 11% ~05s
|++++++ | 12% ~04s
|+++++++ | 13% ~04s
|+++++++ | 14% ~04s
|++++++++ | 15% ~04s
|++++++++ | 16% ~04s
|+++++++++ | 17% ~04s
|+++++++++ | 18% ~04s
|++++++++++ | 19% ~04s
|++++++++++ | 20% ~04s
|+++++++++++ | 21% ~04s
|++++++++++++ | 22% ~04s
|++++++++++++ | 23% ~04s
|+++++++++++++ | 24% ~04s
|+++++++++++++ | 25% ~04s
|++++++++++++++ | 26% ~04s
|++++++++++++++ | 27% ~04s
|+++++++++++++++ | 28% ~04s
|+++++++++++++++ | 29% ~04s
|++++++++++++++++ | 31% ~03s
|++++++++++++++++ | 32% ~03s
|+++++++++++++++++ | 33% ~03s
|+++++++++++++++++ | 34% ~03s
|++++++++++++++++++ | 35% ~03s
|++++++++++++++++++ | 36% ~03s
|+++++++++++++++++++ | 37% ~03s
|+++++++++++++++++++ | 38% ~03s
|++++++++++++++++++++ | 39% ~03s
|++++++++++++++++++++ | 40% ~03s
|+++++++++++++++++++++ | 41% ~03s
|++++++++++++++++++++++ | 42% ~03s
|++++++++++++++++++++++ | 43% ~03s
|+++++++++++++++++++++++ | 44% ~03s
|+++++++++++++++++++++++ | 45% ~03s
|++++++++++++++++++++++++ | 46% ~03s
|++++++++++++++++++++++++ | 47% ~03s
|+++++++++++++++++++++++++ | 48% ~03s
|+++++++++++++++++++++++++ | 49% ~03s
|++++++++++++++++++++++++++ | 51% ~02s
|++++++++++++++++++++++++++ | 52% ~02s
|+++++++++++++++++++++++++++ | 53% ~02s
|+++++++++++++++++++++++++++ | 54% ~02s
|++++++++++++++++++++++++++++ | 55% ~02s
|++++++++++++++++++++++++++++ | 56% ~02s
|+++++++++++++++++++++++++++++ | 57% ~02s
|+++++++++++++++++++++++++++++ | 58% ~02s
|++++++++++++++++++++++++++++++ | 59% ~02s
|++++++++++++++++++++++++++++++ | 60% ~02s
|+++++++++++++++++++++++++++++++ | 61% ~02s
|++++++++++++++++++++++++++++++++ | 62% ~02s
|++++++++++++++++++++++++++++++++ | 63% ~02s
|+++++++++++++++++++++++++++++++++ | 64% ~02s
|+++++++++++++++++++++++++++++++++ | 65% ~02s
|++++++++++++++++++++++++++++++++++ | 66% ~02s
|++++++++++++++++++++++++++++++++++ | 67% ~02s
|+++++++++++++++++++++++++++++++++++ | 68% ~02s
|+++++++++++++++++++++++++++++++++++ | 69% ~02s
|++++++++++++++++++++++++++++++++++++ | 71% ~01s
|++++++++++++++++++++++++++++++++++++ | 72% ~01s
|+++++++++++++++++++++++++++++++++++++ | 73% ~01s
|+++++++++++++++++++++++++++++++++++++ | 74% ~01s
|++++++++++++++++++++++++++++++++++++++ | 75% ~01s
|++++++++++++++++++++++++++++++++++++++ | 76% ~01s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~01s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~01s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~01s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~01s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 84% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 88% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=05s cocl2_markers <- FindAllMarkers(cocl2, only.pos = TRUE, min.pct = 0.25, logfc.threshold = 0.25)Calculating cluster 0
| | 0 % ~calculating
|+ | 1 % ~03s
|++ | 2 % ~04s
|++ | 3 % ~04s
|+++ | 4 % ~04s
|+++ | 5 % ~04s
|++++ | 6 % ~03s
|++++ | 7 % ~03s
|+++++ | 9 % ~03s
|+++++ | 10% ~03s
|++++++ | 11% ~03s
|++++++ | 12% ~03s
|+++++++ | 13% ~03s
|+++++++ | 14% ~03s
|++++++++ | 15% ~03s
|++++++++ | 16% ~03s
|+++++++++ | 17% ~03s
|++++++++++ | 18% ~03s
|++++++++++ | 19% ~03s
|+++++++++++ | 20% ~03s
|+++++++++++ | 21% ~03s
|++++++++++++ | 22% ~03s
|++++++++++++ | 23% ~03s
|+++++++++++++ | 24% ~03s
|+++++++++++++ | 26% ~03s
|++++++++++++++ | 27% ~03s
|++++++++++++++ | 28% ~03s
|+++++++++++++++ | 29% ~03s
|+++++++++++++++ | 30% ~03s
|++++++++++++++++ | 31% ~03s
|++++++++++++++++ | 32% ~03s
|+++++++++++++++++ | 33% ~03s
|++++++++++++++++++ | 34% ~03s
|++++++++++++++++++ | 35% ~02s
|+++++++++++++++++++ | 36% ~02s
|+++++++++++++++++++ | 37% ~02s
|++++++++++++++++++++ | 38% ~02s
|++++++++++++++++++++ | 39% ~02s
|+++++++++++++++++++++ | 40% ~02s
|+++++++++++++++++++++ | 41% ~02s
|++++++++++++++++++++++ | 43% ~02s
|++++++++++++++++++++++ | 44% ~02s
|+++++++++++++++++++++++ | 45% ~02s
|+++++++++++++++++++++++ | 46% ~02s
|++++++++++++++++++++++++ | 47% ~02s
|++++++++++++++++++++++++ | 48% ~02s
|+++++++++++++++++++++++++ | 49% ~02s
|+++++++++++++++++++++++++ | 50% ~02s
|++++++++++++++++++++++++++ | 51% ~02s
|+++++++++++++++++++++++++++ | 52% ~02s
|+++++++++++++++++++++++++++ | 53% ~02s
|++++++++++++++++++++++++++++ | 54% ~02s
|++++++++++++++++++++++++++++ | 55% ~02s
|+++++++++++++++++++++++++++++ | 56% ~02s
|+++++++++++++++++++++++++++++ | 57% ~02s
|++++++++++++++++++++++++++++++ | 59% ~02s
|++++++++++++++++++++++++++++++ | 60% ~02s
|+++++++++++++++++++++++++++++++ | 61% ~01s
|+++++++++++++++++++++++++++++++ | 62% ~01s
|++++++++++++++++++++++++++++++++ | 63% ~01s
|++++++++++++++++++++++++++++++++ | 64% ~01s
|+++++++++++++++++++++++++++++++++ | 65% ~01s
|+++++++++++++++++++++++++++++++++ | 66% ~01s
|++++++++++++++++++++++++++++++++++ | 67% ~01s
|+++++++++++++++++++++++++++++++++++ | 68% ~01s
|+++++++++++++++++++++++++++++++++++ | 69% ~01s
|++++++++++++++++++++++++++++++++++++ | 70% ~01s
|++++++++++++++++++++++++++++++++++++ | 71% ~01s
|+++++++++++++++++++++++++++++++++++++ | 72% ~01s
|+++++++++++++++++++++++++++++++++++++ | 73% ~01s
|++++++++++++++++++++++++++++++++++++++ | 74% ~01s
|++++++++++++++++++++++++++++++++++++++ | 76% ~01s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~01s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~01s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~01s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~01s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 84% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=04s Calculating cluster 1
| | 0 % ~calculating
|+ | 1 % ~01s
|++ | 2 % ~01s
|++ | 4 % ~01s
|+++ | 5 % ~01s
|++++ | 6 % ~01s
|++++ | 8 % ~01s
|+++++ | 9 % ~01s
|+++++ | 10% ~01s
|++++++ | 11% ~01s
|+++++++ | 12% ~01s
|+++++++ | 14% ~01s
|++++++++ | 15% ~01s
|+++++++++ | 16% ~01s
|+++++++++ | 18% ~01s
|++++++++++ | 19% ~01s
|++++++++++ | 20% ~01s
|+++++++++++ | 21% ~01s
|++++++++++++ | 22% ~01s
|++++++++++++ | 24% ~01s
|+++++++++++++ | 25% ~01s
|++++++++++++++ | 26% ~01s
|++++++++++++++ | 28% ~01s
|+++++++++++++++ | 29% ~01s
|+++++++++++++++ | 30% ~01s
|++++++++++++++++ | 31% ~01s
|+++++++++++++++++ | 32% ~01s
|+++++++++++++++++ | 34% ~01s
|++++++++++++++++++ | 35% ~01s
|+++++++++++++++++++ | 36% ~01s
|+++++++++++++++++++ | 38% ~01s
|++++++++++++++++++++ | 39% ~01s
|++++++++++++++++++++ | 40% ~01s
|+++++++++++++++++++++ | 41% ~01s
|++++++++++++++++++++++ | 42% ~01s
|++++++++++++++++++++++ | 44% ~01s
|+++++++++++++++++++++++ | 45% ~01s
|++++++++++++++++++++++++ | 46% ~01s
|++++++++++++++++++++++++ | 48% ~01s
|+++++++++++++++++++++++++ | 49% ~01s
|+++++++++++++++++++++++++ | 50% ~01s
|++++++++++++++++++++++++++ | 51% ~01s
|+++++++++++++++++++++++++++ | 52% ~01s
|+++++++++++++++++++++++++++ | 54% ~01s
|++++++++++++++++++++++++++++ | 55% ~01s
|+++++++++++++++++++++++++++++ | 56% ~01s
|+++++++++++++++++++++++++++++ | 58% ~01s
|++++++++++++++++++++++++++++++ | 59% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++ | 61% ~00s
|++++++++++++++++++++++++++++++++ | 62% ~00s
|++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++ | 65% ~00s
|++++++++++++++++++++++++++++++++++ | 66% ~00s
|++++++++++++++++++++++++++++++++++ | 68% ~00s
|+++++++++++++++++++++++++++++++++++ | 69% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++ | 71% ~00s
|+++++++++++++++++++++++++++++++++++++ | 72% ~00s
|+++++++++++++++++++++++++++++++++++++ | 74% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 76% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=01s Calculating cluster 2
| | 0 % ~calculating
|+ | 1 % ~02s
|++ | 3 % ~01m 03s
|++ | 4 % ~42s
|+++ | 5 % ~32s
|++++ | 7 % ~25s
|++++ | 8 % ~21s
|+++++ | 9 % ~18s
|++++++ | 11% ~16s
|++++++ | 12% ~14s
|+++++++ | 13% ~13s
|++++++++ | 14% ~11s
|++++++++ | 16% ~10s
|+++++++++ | 17% ~10s
|++++++++++ | 18% ~09s
|++++++++++ | 20% ~08s
|+++++++++++ | 21% ~08s
|++++++++++++ | 22% ~07s
|++++++++++++ | 24% ~07s
|+++++++++++++ | 25% ~06s
|++++++++++++++ | 26% ~06s
|++++++++++++++ | 28% ~06s
|+++++++++++++++ | 29% ~05s
|++++++++++++++++ | 30% ~05s
|++++++++++++++++ | 32% ~05s
|+++++++++++++++++ | 33% ~05s
|++++++++++++++++++ | 34% ~04s
|++++++++++++++++++ | 36% ~04s
|+++++++++++++++++++ | 37% ~04s
|++++++++++++++++++++ | 38% ~04s
|++++++++++++++++++++ | 39% ~04s
|+++++++++++++++++++++ | 41% ~03s
|++++++++++++++++++++++ | 42% ~03s
|++++++++++++++++++++++ | 43% ~03s
|+++++++++++++++++++++++ | 45% ~03s
|++++++++++++++++++++++++ | 46% ~03s
|++++++++++++++++++++++++ | 47% ~03s
|+++++++++++++++++++++++++ | 49% ~03s
|+++++++++++++++++++++++++ | 50% ~03s
|++++++++++++++++++++++++++ | 51% ~02s
|+++++++++++++++++++++++++++ | 53% ~02s
|+++++++++++++++++++++++++++ | 54% ~02s
|++++++++++++++++++++++++++++ | 55% ~02s
|+++++++++++++++++++++++++++++ | 57% ~02s
|+++++++++++++++++++++++++++++ | 58% ~02s
|++++++++++++++++++++++++++++++ | 59% ~02s
|+++++++++++++++++++++++++++++++ | 61% ~02s
|+++++++++++++++++++++++++++++++ | 62% ~02s
|++++++++++++++++++++++++++++++++ | 63% ~02s
|+++++++++++++++++++++++++++++++++ | 64% ~02s
|+++++++++++++++++++++++++++++++++ | 66% ~01s
|++++++++++++++++++++++++++++++++++ | 67% ~01s
|+++++++++++++++++++++++++++++++++++ | 68% ~01s
|+++++++++++++++++++++++++++++++++++ | 70% ~01s
|++++++++++++++++++++++++++++++++++++ | 71% ~01s
|+++++++++++++++++++++++++++++++++++++ | 72% ~01s
|+++++++++++++++++++++++++++++++++++++ | 74% ~01s
|++++++++++++++++++++++++++++++++++++++ | 75% ~01s
|+++++++++++++++++++++++++++++++++++++++ | 76% ~01s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~01s
|+++++++++++++++++++++++++++++++++++++++++ | 80% ~01s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~01s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 84% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=03s Calculating cluster 3
| | 0 % ~calculating
|+ | 1 % ~04s
|++ | 2 % ~04s
|++ | 3 % ~04s
|+++ | 4 % ~04s
|+++ | 5 % ~04s
|++++ | 7 % ~04s
|++++ | 8 % ~04s
|+++++ | 9 % ~04s
|+++++ | 10% ~04s
|++++++ | 11% ~03s
|+++++++ | 12% ~03s
|+++++++ | 13% ~03s
|++++++++ | 14% ~03s
|++++++++ | 15% ~03s
|+++++++++ | 16% ~03s
|+++++++++ | 18% ~03s
|++++++++++ | 19% ~03s
|++++++++++ | 20% ~03s
|+++++++++++ | 21% ~03s
|+++++++++++ | 22% ~03s
|++++++++++++ | 23% ~03s
|+++++++++++++ | 24% ~03s
|+++++++++++++ | 25% ~03s
|++++++++++++++ | 26% ~03s
|++++++++++++++ | 27% ~03s
|+++++++++++++++ | 29% ~03s
|+++++++++++++++ | 30% ~03s
|++++++++++++++++ | 31% ~03s
|++++++++++++++++ | 32% ~03s
|+++++++++++++++++ | 33% ~03s
|++++++++++++++++++ | 34% ~03s
|++++++++++++++++++ | 35% ~03s
|+++++++++++++++++++ | 36% ~02s
|+++++++++++++++++++ | 37% ~02s
|++++++++++++++++++++ | 38% ~02s
|++++++++++++++++++++ | 40% ~02s
|+++++++++++++++++++++ | 41% ~02s
|+++++++++++++++++++++ | 42% ~02s
|++++++++++++++++++++++ | 43% ~02s
|++++++++++++++++++++++ | 44% ~02s
|+++++++++++++++++++++++ | 45% ~02s
|++++++++++++++++++++++++ | 46% ~02s
|++++++++++++++++++++++++ | 47% ~02s
|+++++++++++++++++++++++++ | 48% ~02s
|+++++++++++++++++++++++++ | 49% ~02s
|++++++++++++++++++++++++++ | 51% ~02s
|++++++++++++++++++++++++++ | 52% ~02s
|+++++++++++++++++++++++++++ | 53% ~02s
|+++++++++++++++++++++++++++ | 54% ~02s
|++++++++++++++++++++++++++++ | 55% ~02s
|+++++++++++++++++++++++++++++ | 56% ~02s
|+++++++++++++++++++++++++++++ | 57% ~02s
|++++++++++++++++++++++++++++++ | 58% ~02s
|++++++++++++++++++++++++++++++ | 59% ~02s
|+++++++++++++++++++++++++++++++ | 60% ~02s
|+++++++++++++++++++++++++++++++ | 62% ~01s
|++++++++++++++++++++++++++++++++ | 63% ~01s
|++++++++++++++++++++++++++++++++ | 64% ~01s
|+++++++++++++++++++++++++++++++++ | 65% ~01s
|+++++++++++++++++++++++++++++++++ | 66% ~01s
|++++++++++++++++++++++++++++++++++ | 67% ~01s
|+++++++++++++++++++++++++++++++++++ | 68% ~01s
|+++++++++++++++++++++++++++++++++++ | 69% ~01s
|++++++++++++++++++++++++++++++++++++ | 70% ~01s
|++++++++++++++++++++++++++++++++++++ | 71% ~01s
|+++++++++++++++++++++++++++++++++++++ | 73% ~01s
|+++++++++++++++++++++++++++++++++++++ | 74% ~01s
|++++++++++++++++++++++++++++++++++++++ | 75% ~01s
|++++++++++++++++++++++++++++++++++++++ | 76% ~01s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 78% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~01s
|+++++++++++++++++++++++++++++++++++++++++ | 80% ~01s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~01s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~01s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=04s Calculating cluster 4
| | 0 % ~calculating
|+ | 1 % ~02m 19s
|+ | 2 % ~01m 10s
|++ | 3 % ~47s
|++ | 4 % ~36s
|+++ | 5 % ~29s
|+++ | 6 % ~24s
|++++ | 7 % ~21s
|++++ | 8 % ~18s
|+++++ | 9 % ~16s
|+++++ | 10% ~15s
|++++++ | 11% ~13s
|++++++ | 12% ~12s
|+++++++ | 13% ~11s
|+++++++ | 14% ~11s
|++++++++ | 15% ~10s
|++++++++ | 16% ~09s
|+++++++++ | 17% ~09s
|+++++++++ | 18% ~08s
|++++++++++ | 19% ~08s
|++++++++++ | 20% ~08s
|+++++++++++ | 21% ~07s
|+++++++++++ | 22% ~07s
|++++++++++++ | 23% ~07s
|++++++++++++ | 24% ~06s
|+++++++++++++ | 25% ~06s
|+++++++++++++ | 26% ~06s
|++++++++++++++ | 27% ~06s
|++++++++++++++ | 28% ~05s
|+++++++++++++++ | 29% ~05s
|+++++++++++++++ | 30% ~05s
|++++++++++++++++ | 31% ~05s
|++++++++++++++++ | 32% ~05s
|+++++++++++++++++ | 33% ~05s
|+++++++++++++++++ | 34% ~04s
|++++++++++++++++++ | 35% ~04s
|++++++++++++++++++ | 36% ~04s
|+++++++++++++++++++ | 37% ~04s
|+++++++++++++++++++ | 38% ~04s
|++++++++++++++++++++ | 39% ~04s
|++++++++++++++++++++ | 40% ~04s
|+++++++++++++++++++++ | 41% ~04s
|+++++++++++++++++++++ | 42% ~03s
|++++++++++++++++++++++ | 43% ~03s
|++++++++++++++++++++++ | 44% ~03s
|+++++++++++++++++++++++ | 45% ~03s
|+++++++++++++++++++++++ | 46% ~03s
|++++++++++++++++++++++++ | 47% ~03s
|++++++++++++++++++++++++ | 48% ~03s
|+++++++++++++++++++++++++ | 49% ~03s
|+++++++++++++++++++++++++ | 50% ~03s
|++++++++++++++++++++++++++ | 51% ~03s
|++++++++++++++++++++++++++ | 52% ~03s
|+++++++++++++++++++++++++++ | 53% ~02s
|+++++++++++++++++++++++++++ | 54% ~02s
|++++++++++++++++++++++++++++ | 55% ~02s
|++++++++++++++++++++++++++++ | 56% ~02s
|+++++++++++++++++++++++++++++ | 57% ~02s
|+++++++++++++++++++++++++++++ | 58% ~02s
|++++++++++++++++++++++++++++++ | 59% ~02s
|++++++++++++++++++++++++++++++ | 60% ~02s
|+++++++++++++++++++++++++++++++ | 61% ~02s
|+++++++++++++++++++++++++++++++ | 62% ~02s
|++++++++++++++++++++++++++++++++ | 63% ~02s
|++++++++++++++++++++++++++++++++ | 64% ~02s
|+++++++++++++++++++++++++++++++++ | 65% ~02s
|+++++++++++++++++++++++++++++++++ | 66% ~02s
|++++++++++++++++++++++++++++++++++ | 67% ~02s
|++++++++++++++++++++++++++++++++++ | 68% ~01s
|+++++++++++++++++++++++++++++++++++ | 69% ~01s
|+++++++++++++++++++++++++++++++++++ | 70% ~01s
|++++++++++++++++++++++++++++++++++++ | 71% ~01s
|++++++++++++++++++++++++++++++++++++ | 72% ~01s
|+++++++++++++++++++++++++++++++++++++ | 73% ~01s
|+++++++++++++++++++++++++++++++++++++ | 74% ~01s
|++++++++++++++++++++++++++++++++++++++ | 75% ~01s
|++++++++++++++++++++++++++++++++++++++ | 76% ~01s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~01s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~01s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~01s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~01s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~01s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=04s Calculating cluster 5
| | 0 % ~calculating
|+ | 1 % ~13s
|++ | 3 % ~08s
|++ | 4 % ~06s
|+++ | 5 % ~05s
|++++ | 6 % ~04s
|++++ | 8 % ~04s
|+++++ | 9 % ~04s
|++++++ | 10% ~03s
|++++++ | 11% ~03s
|+++++++ | 13% ~03s
|+++++++ | 14% ~03s
|++++++++ | 15% ~03s
|+++++++++ | 16% ~03s
|+++++++++ | 18% ~03s
|++++++++++ | 19% ~03s
|+++++++++++ | 20% ~03s
|+++++++++++ | 22% ~03s
|++++++++++++ | 23% ~02s
|+++++++++++++ | 24% ~02s
|+++++++++++++ | 25% ~02s
|++++++++++++++ | 27% ~02s
|++++++++++++++ | 28% ~02s
|+++++++++++++++ | 29% ~02s
|++++++++++++++++ | 30% ~02s
|++++++++++++++++ | 32% ~02s
|+++++++++++++++++ | 33% ~02s
|++++++++++++++++++ | 34% ~02s
|++++++++++++++++++ | 35% ~02s
|+++++++++++++++++++ | 37% ~02s
|+++++++++++++++++++ | 38% ~02s
|++++++++++++++++++++ | 39% ~02s
|+++++++++++++++++++++ | 41% ~02s
|+++++++++++++++++++++ | 42% ~02s
|++++++++++++++++++++++ | 43% ~02s
|+++++++++++++++++++++++ | 44% ~02s
|+++++++++++++++++++++++ | 46% ~02s
|++++++++++++++++++++++++ | 47% ~02s
|+++++++++++++++++++++++++ | 48% ~01s
|+++++++++++++++++++++++++ | 49% ~01s
|++++++++++++++++++++++++++ | 51% ~01s
|++++++++++++++++++++++++++ | 52% ~01s
|+++++++++++++++++++++++++++ | 53% ~01s
|++++++++++++++++++++++++++++ | 54% ~01s
|++++++++++++++++++++++++++++ | 56% ~01s
|+++++++++++++++++++++++++++++ | 57% ~01s
|++++++++++++++++++++++++++++++ | 58% ~01s
|++++++++++++++++++++++++++++++ | 59% ~01s
|+++++++++++++++++++++++++++++++ | 61% ~01s
|++++++++++++++++++++++++++++++++ | 62% ~01s
|++++++++++++++++++++++++++++++++ | 63% ~01s
|+++++++++++++++++++++++++++++++++ | 65% ~01s
|+++++++++++++++++++++++++++++++++ | 66% ~01s
|++++++++++++++++++++++++++++++++++ | 67% ~01s
|+++++++++++++++++++++++++++++++++++ | 68% ~01s
|+++++++++++++++++++++++++++++++++++ | 70% ~01s
|++++++++++++++++++++++++++++++++++++ | 71% ~01s
|+++++++++++++++++++++++++++++++++++++ | 72% ~01s
|+++++++++++++++++++++++++++++++++++++ | 73% ~01s
|++++++++++++++++++++++++++++++++++++++ | 75% ~01s
|++++++++++++++++++++++++++++++++++++++ | 76% ~01s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 78% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~01s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~01s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=03s Calculating cluster 6
| | 0 % ~calculating
|+ | 1 % ~02s
|++ | 2 % ~02s
|++ | 3 % ~02s
|+++ | 4 % ~02s
|+++ | 5 % ~02s
|++++ | 6 % ~02s
|++++ | 7 % ~02s
|+++++ | 9 % ~02s
|+++++ | 10% ~02s
|++++++ | 11% ~02s
|++++++ | 12% ~02s
|+++++++ | 13% ~02s
|+++++++ | 14% ~02s
|++++++++ | 15% ~02s
|++++++++ | 16% ~02s
|+++++++++ | 17% ~02s
|++++++++++ | 18% ~02s
|++++++++++ | 19% ~02s
|+++++++++++ | 20% ~02s
|+++++++++++ | 21% ~02s
|++++++++++++ | 22% ~01s
|++++++++++++ | 23% ~01s
|+++++++++++++ | 24% ~01s
|+++++++++++++ | 26% ~01s
|++++++++++++++ | 27% ~01s
|++++++++++++++ | 28% ~01s
|+++++++++++++++ | 29% ~01s
|+++++++++++++++ | 30% ~01s
|++++++++++++++++ | 31% ~01s
|++++++++++++++++ | 32% ~01s
|+++++++++++++++++ | 33% ~01s
|++++++++++++++++++ | 34% ~01s
|++++++++++++++++++ | 35% ~01s
|+++++++++++++++++++ | 36% ~01s
|+++++++++++++++++++ | 37% ~01s
|++++++++++++++++++++ | 38% ~01s
|++++++++++++++++++++ | 39% ~01s
|+++++++++++++++++++++ | 40% ~01s
|+++++++++++++++++++++ | 41% ~01s
|++++++++++++++++++++++ | 43% ~01s
|++++++++++++++++++++++ | 44% ~01s
|+++++++++++++++++++++++ | 45% ~01s
|+++++++++++++++++++++++ | 46% ~01s
|++++++++++++++++++++++++ | 47% ~01s
|++++++++++++++++++++++++ | 48% ~01s
|+++++++++++++++++++++++++ | 49% ~01s
|+++++++++++++++++++++++++ | 50% ~01s
|++++++++++++++++++++++++++ | 51% ~01s
|+++++++++++++++++++++++++++ | 52% ~01s
|+++++++++++++++++++++++++++ | 53% ~01s
|++++++++++++++++++++++++++++ | 54% ~01s
|++++++++++++++++++++++++++++ | 55% ~01s
|+++++++++++++++++++++++++++++ | 56% ~01s
|+++++++++++++++++++++++++++++ | 57% ~01s
|++++++++++++++++++++++++++++++ | 59% ~01s
|++++++++++++++++++++++++++++++ | 60% ~01s
|+++++++++++++++++++++++++++++++ | 61% ~01s
|+++++++++++++++++++++++++++++++ | 62% ~01s
|++++++++++++++++++++++++++++++++ | 63% ~01s
|++++++++++++++++++++++++++++++++ | 64% ~01s
|+++++++++++++++++++++++++++++++++ | 65% ~01s
|+++++++++++++++++++++++++++++++++ | 66% ~01s
|++++++++++++++++++++++++++++++++++ | 67% ~01s
|+++++++++++++++++++++++++++++++++++ | 68% ~01s
|+++++++++++++++++++++++++++++++++++ | 69% ~01s
|++++++++++++++++++++++++++++++++++++ | 70% ~01s
|++++++++++++++++++++++++++++++++++++ | 71% ~01s
|+++++++++++++++++++++++++++++++++++++ | 72% ~01s
|+++++++++++++++++++++++++++++++++++++ | 73% ~01s
|++++++++++++++++++++++++++++++++++++++ | 74% ~00s
|++++++++++++++++++++++++++++++++++++++ | 76% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 84% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=02s Calculating cluster 7
| | 0 % ~calculating
|+ | 1 % ~11s
|+ | 2 % ~11s
|++ | 3 % ~11s
|++ | 4 % ~11s
|+++ | 5 % ~11s
|+++ | 6 % ~11s
|++++ | 7 % ~11s
|++++ | 8 % ~11s
|+++++ | 9 % ~11s
|+++++ | 10% ~11s
|++++++ | 11% ~10s
|++++++ | 12% ~10s
|+++++++ | 13% ~10s
|+++++++ | 14% ~10s
|++++++++ | 15% ~10s
|++++++++ | 16% ~10s
|+++++++++ | 17% ~10s
|+++++++++ | 18% ~10s
|++++++++++ | 19% ~10s
|++++++++++ | 20% ~09s
|+++++++++++ | 21% ~09s
|+++++++++++ | 22% ~09s
|++++++++++++ | 23% ~10s
|++++++++++++ | 24% ~10s
|+++++++++++++ | 25% ~10s
|+++++++++++++ | 26% ~09s
|++++++++++++++ | 27% ~09s
|++++++++++++++ | 28% ~09s
|+++++++++++++++ | 29% ~09s
|+++++++++++++++ | 30% ~09s
|++++++++++++++++ | 31% ~09s
|++++++++++++++++ | 32% ~09s
|+++++++++++++++++ | 33% ~08s
|+++++++++++++++++ | 34% ~08s
|++++++++++++++++++ | 35% ~08s
|++++++++++++++++++ | 36% ~08s
|+++++++++++++++++++ | 37% ~08s
|+++++++++++++++++++ | 38% ~08s
|++++++++++++++++++++ | 39% ~08s
|++++++++++++++++++++ | 40% ~07s
|+++++++++++++++++++++ | 41% ~07s
|+++++++++++++++++++++ | 42% ~07s
|++++++++++++++++++++++ | 43% ~07s
|++++++++++++++++++++++ | 44% ~07s
|+++++++++++++++++++++++ | 45% ~07s
|+++++++++++++++++++++++ | 46% ~07s
|++++++++++++++++++++++++ | 47% ~06s
|++++++++++++++++++++++++ | 48% ~06s
|+++++++++++++++++++++++++ | 49% ~06s
|+++++++++++++++++++++++++ | 50% ~06s
|++++++++++++++++++++++++++ | 51% ~06s
|++++++++++++++++++++++++++ | 52% ~06s
|+++++++++++++++++++++++++++ | 53% ~06s
|+++++++++++++++++++++++++++ | 54% ~06s
|++++++++++++++++++++++++++++ | 55% ~05s
|++++++++++++++++++++++++++++ | 56% ~05s
|+++++++++++++++++++++++++++++ | 57% ~05s
|+++++++++++++++++++++++++++++ | 58% ~05s
|++++++++++++++++++++++++++++++ | 59% ~05s
|++++++++++++++++++++++++++++++ | 60% ~05s
|+++++++++++++++++++++++++++++++ | 61% ~05s
|+++++++++++++++++++++++++++++++ | 62% ~05s
|++++++++++++++++++++++++++++++++ | 63% ~04s
|++++++++++++++++++++++++++++++++ | 64% ~04s
|+++++++++++++++++++++++++++++++++ | 65% ~04s
|+++++++++++++++++++++++++++++++++ | 66% ~04s
|++++++++++++++++++++++++++++++++++ | 67% ~04s
|++++++++++++++++++++++++++++++++++ | 68% ~04s
|+++++++++++++++++++++++++++++++++++ | 69% ~04s
|+++++++++++++++++++++++++++++++++++ | 70% ~04s
|++++++++++++++++++++++++++++++++++++ | 71% ~03s
|++++++++++++++++++++++++++++++++++++ | 72% ~03s
|+++++++++++++++++++++++++++++++++++++ | 73% ~03s
|+++++++++++++++++++++++++++++++++++++ | 74% ~03s
|++++++++++++++++++++++++++++++++++++++ | 75% ~03s
|++++++++++++++++++++++++++++++++++++++ | 76% ~03s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~03s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~03s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~03s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~02s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~02s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~02s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~02s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~02s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~02s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~02s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~02s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=12s Calculating cluster 8
| | 0 % ~calculating
|+ | 1 % ~04s
|++ | 2 % ~04s
|++ | 3 % ~04s
|+++ | 5 % ~04s
|+++ | 6 % ~04s
|++++ | 7 % ~04s
|+++++ | 8 % ~04s
|+++++ | 9 % ~04s
|++++++ | 10% ~04s
|++++++ | 11% ~03s
|+++++++ | 13% ~03s
|+++++++ | 14% ~03s
|++++++++ | 15% ~03s
|+++++++++ | 16% ~03s
|+++++++++ | 17% ~03s
|++++++++++ | 18% ~03s
|++++++++++ | 20% ~03s
|+++++++++++ | 21% ~03s
|+++++++++++ | 22% ~03s
|++++++++++++ | 23% ~03s
|+++++++++++++ | 24% ~03s
|+++++++++++++ | 25% ~03s
|++++++++++++++ | 26% ~03s
|++++++++++++++ | 28% ~03s
|+++++++++++++++ | 29% ~03s
|+++++++++++++++ | 30% ~03s
|++++++++++++++++ | 31% ~03s
|+++++++++++++++++ | 32% ~03s
|+++++++++++++++++ | 33% ~03s
|++++++++++++++++++ | 34% ~02s
|++++++++++++++++++ | 36% ~02s
|+++++++++++++++++++ | 37% ~02s
|+++++++++++++++++++ | 38% ~02s
|++++++++++++++++++++ | 39% ~02s
|+++++++++++++++++++++ | 40% ~02s
|+++++++++++++++++++++ | 41% ~02s
|++++++++++++++++++++++ | 43% ~02s
|++++++++++++++++++++++ | 44% ~02s
|+++++++++++++++++++++++ | 45% ~02s
|+++++++++++++++++++++++ | 46% ~02s
|++++++++++++++++++++++++ | 47% ~02s
|+++++++++++++++++++++++++ | 48% ~02s
|+++++++++++++++++++++++++ | 49% ~02s
|++++++++++++++++++++++++++ | 51% ~02s
|++++++++++++++++++++++++++ | 52% ~02s
|+++++++++++++++++++++++++++ | 53% ~02s
|++++++++++++++++++++++++++++ | 54% ~02s
|++++++++++++++++++++++++++++ | 55% ~02s
|+++++++++++++++++++++++++++++ | 56% ~02s
|+++++++++++++++++++++++++++++ | 57% ~02s
|++++++++++++++++++++++++++++++ | 59% ~02s
|++++++++++++++++++++++++++++++ | 60% ~02s
|+++++++++++++++++++++++++++++++ | 61% ~01s
|++++++++++++++++++++++++++++++++ | 62% ~01s
|++++++++++++++++++++++++++++++++ | 63% ~01s
|+++++++++++++++++++++++++++++++++ | 64% ~01s
|+++++++++++++++++++++++++++++++++ | 66% ~01s
|++++++++++++++++++++++++++++++++++ | 67% ~01s
|++++++++++++++++++++++++++++++++++ | 68% ~01s
|+++++++++++++++++++++++++++++++++++ | 69% ~01s
|++++++++++++++++++++++++++++++++++++ | 70% ~01s
|++++++++++++++++++++++++++++++++++++ | 71% ~01s
|+++++++++++++++++++++++++++++++++++++ | 72% ~01s
|+++++++++++++++++++++++++++++++++++++ | 74% ~01s
|++++++++++++++++++++++++++++++++++++++ | 75% ~01s
|++++++++++++++++++++++++++++++++++++++ | 76% ~01s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 78% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~01s
|+++++++++++++++++++++++++++++++++++++++++ | 80% ~01s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~01s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~01s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=04s Calculating cluster 9
| | 0 % ~calculating
|+ | 1 % ~08s
|++ | 2 % ~08s
|++ | 3 % ~08s
|+++ | 4 % ~08s
|+++ | 5 % ~08s
|++++ | 6 % ~08s
|++++ | 7 % ~07s
|+++++ | 8 % ~07s
|+++++ | 9 % ~07s
|++++++ | 10% ~07s
|++++++ | 11% ~07s
|+++++++ | 12% ~07s
|+++++++ | 13% ~07s
|++++++++ | 14% ~07s
|++++++++ | 15% ~07s
|+++++++++ | 16% ~07s
|+++++++++ | 17% ~07s
|++++++++++ | 18% ~07s
|++++++++++ | 19% ~06s
|+++++++++++ | 20% ~06s
|+++++++++++ | 21% ~06s
|++++++++++++ | 22% ~06s
|++++++++++++ | 23% ~06s
|+++++++++++++ | 24% ~06s
|+++++++++++++ | 25% ~06s
|++++++++++++++ | 26% ~06s
|++++++++++++++ | 27% ~06s
|+++++++++++++++ | 28% ~06s
|+++++++++++++++ | 29% ~06s
|++++++++++++++++ | 30% ~06s
|++++++++++++++++ | 31% ~05s
|+++++++++++++++++ | 32% ~05s
|+++++++++++++++++ | 33% ~05s
|++++++++++++++++++ | 34% ~05s
|++++++++++++++++++ | 35% ~05s
|+++++++++++++++++++ | 36% ~05s
|+++++++++++++++++++ | 37% ~05s
|++++++++++++++++++++ | 38% ~05s
|++++++++++++++++++++ | 39% ~05s
|+++++++++++++++++++++ | 40% ~05s
|+++++++++++++++++++++ | 41% ~05s
|++++++++++++++++++++++ | 42% ~05s
|++++++++++++++++++++++ | 43% ~05s
|+++++++++++++++++++++++ | 44% ~04s
|+++++++++++++++++++++++ | 45% ~04s
|++++++++++++++++++++++++ | 46% ~04s
|++++++++++++++++++++++++ | 47% ~04s
|+++++++++++++++++++++++++ | 48% ~04s
|+++++++++++++++++++++++++ | 49% ~04s
|++++++++++++++++++++++++++ | 51% ~04s
|++++++++++++++++++++++++++ | 52% ~04s
|+++++++++++++++++++++++++++ | 53% ~04s
|+++++++++++++++++++++++++++ | 54% ~04s
|++++++++++++++++++++++++++++ | 55% ~04s
|++++++++++++++++++++++++++++ | 56% ~04s
|+++++++++++++++++++++++++++++ | 57% ~03s
|+++++++++++++++++++++++++++++ | 58% ~03s
|++++++++++++++++++++++++++++++ | 59% ~03s
|++++++++++++++++++++++++++++++ | 60% ~03s
|+++++++++++++++++++++++++++++++ | 61% ~03s
|+++++++++++++++++++++++++++++++ | 62% ~03s
|++++++++++++++++++++++++++++++++ | 63% ~03s
|++++++++++++++++++++++++++++++++ | 64% ~03s
|+++++++++++++++++++++++++++++++++ | 65% ~03s
|+++++++++++++++++++++++++++++++++ | 66% ~03s
|++++++++++++++++++++++++++++++++++ | 67% ~03s
|++++++++++++++++++++++++++++++++++ | 68% ~03s
|+++++++++++++++++++++++++++++++++++ | 69% ~03s
|+++++++++++++++++++++++++++++++++++ | 70% ~02s
|++++++++++++++++++++++++++++++++++++ | 71% ~02s
|++++++++++++++++++++++++++++++++++++ | 72% ~02s
|+++++++++++++++++++++++++++++++++++++ | 73% ~02s
|+++++++++++++++++++++++++++++++++++++ | 74% ~02s
|++++++++++++++++++++++++++++++++++++++ | 75% ~02s
|++++++++++++++++++++++++++++++++++++++ | 76% ~02s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~02s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~02s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~02s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~02s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~02s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~01s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~01s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=08s dabtram_markers <- FindAllMarkers(dabtram, only.pos = TRUE, min.pct = 0.25, logfc.threshold = 0.25)Calculating cluster 0
| | 0 % ~calculating
|+ | 1 % ~04s
|++ | 2 % ~03s
|++ | 3 % ~03s
|+++ | 5 % ~03s
|+++ | 6 % ~03s
|++++ | 7 % ~03s
|++++ | 8 % ~03s
|+++++ | 9 % ~03s
|++++++ | 10% ~03s
|++++++ | 11% ~03s
|+++++++ | 12% ~03s
|+++++++ | 14% ~03s
|++++++++ | 15% ~03s
|++++++++ | 16% ~03s
|+++++++++ | 17% ~03s
|++++++++++ | 18% ~03s
|++++++++++ | 19% ~03s
|+++++++++++ | 20% ~03s
|+++++++++++ | 22% ~03s
|++++++++++++ | 23% ~03s
|++++++++++++ | 24% ~02s
|+++++++++++++ | 25% ~02s
|++++++++++++++ | 26% ~02s
|++++++++++++++ | 27% ~02s
|+++++++++++++++ | 28% ~02s
|+++++++++++++++ | 30% ~02s
|++++++++++++++++ | 31% ~02s
|++++++++++++++++ | 32% ~02s
|+++++++++++++++++ | 33% ~02s
|++++++++++++++++++ | 34% ~02s
|++++++++++++++++++ | 35% ~02s
|+++++++++++++++++++ | 36% ~02s
|+++++++++++++++++++ | 38% ~02s
|++++++++++++++++++++ | 39% ~02s
|++++++++++++++++++++ | 40% ~02s
|+++++++++++++++++++++ | 41% ~02s
|++++++++++++++++++++++ | 42% ~02s
|++++++++++++++++++++++ | 43% ~02s
|+++++++++++++++++++++++ | 44% ~02s
|+++++++++++++++++++++++ | 45% ~02s
|++++++++++++++++++++++++ | 47% ~02s
|++++++++++++++++++++++++ | 48% ~02s
|+++++++++++++++++++++++++ | 49% ~02s
|+++++++++++++++++++++++++ | 50% ~02s
|++++++++++++++++++++++++++ | 51% ~02s
|+++++++++++++++++++++++++++ | 52% ~02s
|+++++++++++++++++++++++++++ | 53% ~02s
|++++++++++++++++++++++++++++ | 55% ~01s
|++++++++++++++++++++++++++++ | 56% ~01s
|+++++++++++++++++++++++++++++ | 57% ~01s
|+++++++++++++++++++++++++++++ | 58% ~01s
|++++++++++++++++++++++++++++++ | 59% ~01s
|+++++++++++++++++++++++++++++++ | 60% ~01s
|+++++++++++++++++++++++++++++++ | 61% ~01s
|++++++++++++++++++++++++++++++++ | 62% ~01s
|++++++++++++++++++++++++++++++++ | 64% ~01s
|+++++++++++++++++++++++++++++++++ | 65% ~01s
|+++++++++++++++++++++++++++++++++ | 66% ~01s
|++++++++++++++++++++++++++++++++++ | 67% ~01s
|+++++++++++++++++++++++++++++++++++ | 68% ~01s
|+++++++++++++++++++++++++++++++++++ | 69% ~01s
|++++++++++++++++++++++++++++++++++++ | 70% ~01s
|++++++++++++++++++++++++++++++++++++ | 72% ~01s
|+++++++++++++++++++++++++++++++++++++ | 73% ~01s
|+++++++++++++++++++++++++++++++++++++ | 74% ~01s
|++++++++++++++++++++++++++++++++++++++ | 75% ~01s
|+++++++++++++++++++++++++++++++++++++++ | 76% ~01s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 78% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~01s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~01s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~01s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 84% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=03s Calculating cluster 1
| | 0 % ~calculating
|+ | 1 % ~04s
|++ | 2 % ~04s
|++ | 3 % ~04s
|+++ | 4 % ~04s
|+++ | 5 % ~03s
|++++ | 6 % ~03s
|++++ | 7 % ~03s
|+++++ | 8 % ~03s
|+++++ | 9 % ~03s
|++++++ | 10% ~03s
|++++++ | 11% ~03s
|+++++++ | 12% ~03s
|+++++++ | 13% ~03s
|++++++++ | 14% ~03s
|++++++++ | 15% ~03s
|+++++++++ | 16% ~03s
|+++++++++ | 17% ~03s
|++++++++++ | 18% ~03s
|++++++++++ | 19% ~03s
|+++++++++++ | 20% ~03s
|+++++++++++ | 21% ~03s
|++++++++++++ | 22% ~03s
|++++++++++++ | 23% ~03s
|+++++++++++++ | 24% ~03s
|+++++++++++++ | 26% ~03s
|++++++++++++++ | 27% ~03s
|++++++++++++++ | 28% ~03s
|+++++++++++++++ | 29% ~03s
|+++++++++++++++ | 30% ~02s
|++++++++++++++++ | 31% ~02s
|++++++++++++++++ | 32% ~02s
|+++++++++++++++++ | 33% ~02s
|+++++++++++++++++ | 34% ~02s
|++++++++++++++++++ | 35% ~02s
|++++++++++++++++++ | 36% ~02s
|+++++++++++++++++++ | 37% ~02s
|+++++++++++++++++++ | 38% ~02s
|++++++++++++++++++++ | 39% ~02s
|++++++++++++++++++++ | 40% ~02s
|+++++++++++++++++++++ | 41% ~02s
|+++++++++++++++++++++ | 42% ~02s
|++++++++++++++++++++++ | 43% ~02s
|++++++++++++++++++++++ | 44% ~02s
|+++++++++++++++++++++++ | 45% ~02s
|+++++++++++++++++++++++ | 46% ~02s
|++++++++++++++++++++++++ | 47% ~02s
|++++++++++++++++++++++++ | 48% ~02s
|+++++++++++++++++++++++++ | 49% ~02s
|+++++++++++++++++++++++++ | 50% ~02s
|++++++++++++++++++++++++++ | 51% ~02s
|+++++++++++++++++++++++++++ | 52% ~02s
|+++++++++++++++++++++++++++ | 53% ~02s
|++++++++++++++++++++++++++++ | 54% ~02s
|++++++++++++++++++++++++++++ | 55% ~02s
|+++++++++++++++++++++++++++++ | 56% ~02s
|+++++++++++++++++++++++++++++ | 57% ~02s
|++++++++++++++++++++++++++++++ | 58% ~01s
|++++++++++++++++++++++++++++++ | 59% ~01s
|+++++++++++++++++++++++++++++++ | 60% ~01s
|+++++++++++++++++++++++++++++++ | 61% ~01s
|++++++++++++++++++++++++++++++++ | 62% ~01s
|++++++++++++++++++++++++++++++++ | 63% ~01s
|+++++++++++++++++++++++++++++++++ | 64% ~01s
|+++++++++++++++++++++++++++++++++ | 65% ~01s
|++++++++++++++++++++++++++++++++++ | 66% ~01s
|++++++++++++++++++++++++++++++++++ | 67% ~01s
|+++++++++++++++++++++++++++++++++++ | 68% ~01s
|+++++++++++++++++++++++++++++++++++ | 69% ~01s
|++++++++++++++++++++++++++++++++++++ | 70% ~01s
|++++++++++++++++++++++++++++++++++++ | 71% ~01s
|+++++++++++++++++++++++++++++++++++++ | 72% ~01s
|+++++++++++++++++++++++++++++++++++++ | 73% ~01s
|++++++++++++++++++++++++++++++++++++++ | 74% ~01s
|++++++++++++++++++++++++++++++++++++++ | 76% ~01s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~01s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~01s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~01s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~01s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~01s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=04s Calculating cluster 2
| | 0 % ~calculating
|+ | 1 % ~03s
|++ | 2 % ~04s
|++ | 3 % ~04s
|+++ | 5 % ~04s
|+++ | 6 % ~04s
|++++ | 7 % ~04s
|+++++ | 8 % ~03s
|+++++ | 9 % ~03s
|++++++ | 10% ~03s
|++++++ | 11% ~03s
|+++++++ | 13% ~03s
|+++++++ | 14% ~03s
|++++++++ | 15% ~03s
|+++++++++ | 16% ~03s
|+++++++++ | 17% ~03s
|++++++++++ | 18% ~03s
|++++++++++ | 20% ~03s
|+++++++++++ | 21% ~03s
|+++++++++++ | 22% ~03s
|++++++++++++ | 23% ~03s
|+++++++++++++ | 24% ~03s
|+++++++++++++ | 25% ~03s
|++++++++++++++ | 26% ~03s
|++++++++++++++ | 28% ~03s
|+++++++++++++++ | 29% ~03s
|+++++++++++++++ | 30% ~03s
|++++++++++++++++ | 31% ~03s
|+++++++++++++++++ | 32% ~02s
|+++++++++++++++++ | 33% ~02s
|++++++++++++++++++ | 34% ~02s
|++++++++++++++++++ | 36% ~02s
|+++++++++++++++++++ | 37% ~02s
|+++++++++++++++++++ | 38% ~02s
|++++++++++++++++++++ | 39% ~02s
|+++++++++++++++++++++ | 40% ~02s
|+++++++++++++++++++++ | 41% ~02s
|++++++++++++++++++++++ | 43% ~02s
|++++++++++++++++++++++ | 44% ~02s
|+++++++++++++++++++++++ | 45% ~02s
|+++++++++++++++++++++++ | 46% ~02s
|++++++++++++++++++++++++ | 47% ~02s
|+++++++++++++++++++++++++ | 48% ~02s
|+++++++++++++++++++++++++ | 49% ~02s
|++++++++++++++++++++++++++ | 51% ~02s
|++++++++++++++++++++++++++ | 52% ~02s
|+++++++++++++++++++++++++++ | 53% ~02s
|++++++++++++++++++++++++++++ | 54% ~02s
|++++++++++++++++++++++++++++ | 55% ~02s
|+++++++++++++++++++++++++++++ | 56% ~02s
|+++++++++++++++++++++++++++++ | 57% ~02s
|++++++++++++++++++++++++++++++ | 59% ~01s
|++++++++++++++++++++++++++++++ | 60% ~01s
|+++++++++++++++++++++++++++++++ | 61% ~01s
|++++++++++++++++++++++++++++++++ | 62% ~01s
|++++++++++++++++++++++++++++++++ | 63% ~01s
|+++++++++++++++++++++++++++++++++ | 64% ~01s
|+++++++++++++++++++++++++++++++++ | 66% ~01s
|++++++++++++++++++++++++++++++++++ | 67% ~01s
|++++++++++++++++++++++++++++++++++ | 68% ~01s
|+++++++++++++++++++++++++++++++++++ | 69% ~01s
|++++++++++++++++++++++++++++++++++++ | 70% ~01s
|++++++++++++++++++++++++++++++++++++ | 71% ~01s
|+++++++++++++++++++++++++++++++++++++ | 72% ~01s
|+++++++++++++++++++++++++++++++++++++ | 74% ~01s
|++++++++++++++++++++++++++++++++++++++ | 75% ~01s
|++++++++++++++++++++++++++++++++++++++ | 76% ~01s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 78% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~01s
|+++++++++++++++++++++++++++++++++++++++++ | 80% ~01s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~01s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~01s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=04s Calculating cluster 3
| | 0 % ~calculating
|+ | 1 % ~03s
|++ | 2 % ~02s
|++ | 4 % ~02s
|+++ | 5 % ~02s
|++++ | 6 % ~02s
|++++ | 7 % ~02s
|+++++ | 9 % ~02s
|+++++ | 10% ~02s
|++++++ | 11% ~02s
|+++++++ | 12% ~02s
|+++++++ | 14% ~02s
|++++++++ | 15% ~02s
|+++++++++ | 16% ~02s
|+++++++++ | 17% ~02s
|++++++++++ | 19% ~02s
|++++++++++ | 20% ~02s
|+++++++++++ | 21% ~02s
|++++++++++++ | 22% ~02s
|++++++++++++ | 23% ~02s
|+++++++++++++ | 25% ~02s
|+++++++++++++ | 26% ~02s
|++++++++++++++ | 27% ~02s
|+++++++++++++++ | 28% ~02s
|+++++++++++++++ | 30% ~02s
|++++++++++++++++ | 31% ~02s
|+++++++++++++++++ | 32% ~02s
|+++++++++++++++++ | 33% ~02s
|++++++++++++++++++ | 35% ~01s
|++++++++++++++++++ | 36% ~01s
|+++++++++++++++++++ | 37% ~01s
|++++++++++++++++++++ | 38% ~01s
|++++++++++++++++++++ | 40% ~01s
|+++++++++++++++++++++ | 41% ~01s
|+++++++++++++++++++++ | 42% ~01s
|++++++++++++++++++++++ | 43% ~01s
|+++++++++++++++++++++++ | 44% ~01s
|+++++++++++++++++++++++ | 46% ~01s
|++++++++++++++++++++++++ | 47% ~01s
|+++++++++++++++++++++++++ | 48% ~01s
|+++++++++++++++++++++++++ | 49% ~01s
|++++++++++++++++++++++++++ | 51% ~01s
|++++++++++++++++++++++++++ | 52% ~01s
|+++++++++++++++++++++++++++ | 53% ~01s
|++++++++++++++++++++++++++++ | 54% ~01s
|++++++++++++++++++++++++++++ | 56% ~01s
|+++++++++++++++++++++++++++++ | 57% ~01s
|++++++++++++++++++++++++++++++ | 58% ~01s
|++++++++++++++++++++++++++++++ | 59% ~01s
|+++++++++++++++++++++++++++++++ | 60% ~01s
|+++++++++++++++++++++++++++++++ | 62% ~01s
|++++++++++++++++++++++++++++++++ | 63% ~01s
|+++++++++++++++++++++++++++++++++ | 64% ~01s
|+++++++++++++++++++++++++++++++++ | 65% ~01s
|++++++++++++++++++++++++++++++++++ | 67% ~01s
|++++++++++++++++++++++++++++++++++ | 68% ~01s
|+++++++++++++++++++++++++++++++++++ | 69% ~01s
|++++++++++++++++++++++++++++++++++++ | 70% ~01s
|++++++++++++++++++++++++++++++++++++ | 72% ~01s
|+++++++++++++++++++++++++++++++++++++ | 73% ~01s
|++++++++++++++++++++++++++++++++++++++ | 74% ~01s
|++++++++++++++++++++++++++++++++++++++ | 75% ~01s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~01s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=02s Calculating cluster 4
| | 0 % ~calculating
|+ | 1 % ~08s
|++ | 2 % ~08s
|++ | 3 % ~08s
|+++ | 4 % ~07s
|+++ | 5 % ~07s
|++++ | 6 % ~07s
|++++ | 7 % ~07s
|+++++ | 8 % ~07s
|+++++ | 9 % ~07s
|++++++ | 10% ~07s
|++++++ | 11% ~07s
|+++++++ | 12% ~07s
|+++++++ | 13% ~07s
|++++++++ | 14% ~06s
|++++++++ | 15% ~06s
|+++++++++ | 16% ~06s
|+++++++++ | 17% ~06s
|++++++++++ | 18% ~06s
|++++++++++ | 19% ~06s
|+++++++++++ | 20% ~06s
|+++++++++++ | 21% ~06s
|++++++++++++ | 22% ~06s
|++++++++++++ | 23% ~06s
|+++++++++++++ | 24% ~06s
|+++++++++++++ | 25% ~06s
|++++++++++++++ | 26% ~06s
|++++++++++++++ | 27% ~05s
|+++++++++++++++ | 28% ~05s
|+++++++++++++++ | 29% ~05s
|++++++++++++++++ | 30% ~05s
|++++++++++++++++ | 31% ~05s
|+++++++++++++++++ | 32% ~05s
|+++++++++++++++++ | 33% ~05s
|++++++++++++++++++ | 34% ~05s
|++++++++++++++++++ | 35% ~05s
|+++++++++++++++++++ | 36% ~05s
|+++++++++++++++++++ | 37% ~05s
|++++++++++++++++++++ | 38% ~05s
|++++++++++++++++++++ | 39% ~05s
|+++++++++++++++++++++ | 40% ~04s
|+++++++++++++++++++++ | 41% ~04s
|++++++++++++++++++++++ | 42% ~04s
|++++++++++++++++++++++ | 43% ~04s
|+++++++++++++++++++++++ | 44% ~04s
|+++++++++++++++++++++++ | 45% ~04s
|++++++++++++++++++++++++ | 46% ~04s
|++++++++++++++++++++++++ | 47% ~04s
|+++++++++++++++++++++++++ | 48% ~04s
|+++++++++++++++++++++++++ | 49% ~04s
|++++++++++++++++++++++++++ | 51% ~04s
|++++++++++++++++++++++++++ | 52% ~04s
|+++++++++++++++++++++++++++ | 53% ~04s
|+++++++++++++++++++++++++++ | 54% ~03s
|++++++++++++++++++++++++++++ | 55% ~03s
|++++++++++++++++++++++++++++ | 56% ~03s
|+++++++++++++++++++++++++++++ | 57% ~03s
|+++++++++++++++++++++++++++++ | 58% ~03s
|++++++++++++++++++++++++++++++ | 59% ~03s
|++++++++++++++++++++++++++++++ | 60% ~03s
|+++++++++++++++++++++++++++++++ | 61% ~03s
|+++++++++++++++++++++++++++++++ | 62% ~03s
|++++++++++++++++++++++++++++++++ | 63% ~03s
|++++++++++++++++++++++++++++++++ | 64% ~03s
|+++++++++++++++++++++++++++++++++ | 65% ~03s
|+++++++++++++++++++++++++++++++++ | 66% ~03s
|++++++++++++++++++++++++++++++++++ | 67% ~03s
|++++++++++++++++++++++++++++++++++ | 68% ~02s
|+++++++++++++++++++++++++++++++++++ | 69% ~02s
|+++++++++++++++++++++++++++++++++++ | 70% ~02s
|++++++++++++++++++++++++++++++++++++ | 71% ~02s
|++++++++++++++++++++++++++++++++++++ | 72% ~02s
|+++++++++++++++++++++++++++++++++++++ | 73% ~02s
|+++++++++++++++++++++++++++++++++++++ | 74% ~02s
|++++++++++++++++++++++++++++++++++++++ | 75% ~02s
|++++++++++++++++++++++++++++++++++++++ | 76% ~02s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~02s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~02s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~02s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~02s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~01s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~01s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~01s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=08s Calculating cluster 5
| | 0 % ~calculating
|+ | 1 % ~06s
|++ | 2 % ~06s
|++ | 3 % ~06s
|+++ | 4 % ~06s
|+++ | 5 % ~06s
|++++ | 6 % ~06s
|++++ | 7 % ~06s
|+++++ | 8 % ~06s
|+++++ | 9 % ~05s
|++++++ | 10% ~05s
|++++++ | 11% ~05s
|+++++++ | 12% ~05s
|+++++++ | 13% ~05s
|++++++++ | 14% ~05s
|++++++++ | 15% ~05s
|+++++++++ | 16% ~05s
|+++++++++ | 17% ~05s
|++++++++++ | 18% ~05s
|++++++++++ | 19% ~05s
|+++++++++++ | 20% ~05s
|+++++++++++ | 21% ~05s
|++++++++++++ | 22% ~05s
|++++++++++++ | 23% ~05s
|+++++++++++++ | 24% ~05s
|+++++++++++++ | 26% ~04s
|++++++++++++++ | 27% ~04s
|++++++++++++++ | 28% ~04s
|+++++++++++++++ | 29% ~04s
|+++++++++++++++ | 30% ~04s
|++++++++++++++++ | 31% ~04s
|++++++++++++++++ | 32% ~04s
|+++++++++++++++++ | 33% ~04s
|+++++++++++++++++ | 34% ~04s
|++++++++++++++++++ | 35% ~04s
|++++++++++++++++++ | 36% ~04s
|+++++++++++++++++++ | 37% ~04s
|+++++++++++++++++++ | 38% ~04s
|++++++++++++++++++++ | 39% ~04s
|++++++++++++++++++++ | 40% ~04s
|+++++++++++++++++++++ | 41% ~04s
|+++++++++++++++++++++ | 42% ~03s
|++++++++++++++++++++++ | 43% ~03s
|++++++++++++++++++++++ | 44% ~03s
|+++++++++++++++++++++++ | 45% ~03s
|+++++++++++++++++++++++ | 46% ~03s
|++++++++++++++++++++++++ | 47% ~03s
|++++++++++++++++++++++++ | 48% ~03s
|+++++++++++++++++++++++++ | 49% ~03s
|+++++++++++++++++++++++++ | 50% ~03s
|++++++++++++++++++++++++++ | 51% ~03s
|+++++++++++++++++++++++++++ | 52% ~03s
|+++++++++++++++++++++++++++ | 53% ~03s
|++++++++++++++++++++++++++++ | 54% ~03s
|++++++++++++++++++++++++++++ | 55% ~03s
|+++++++++++++++++++++++++++++ | 56% ~03s
|+++++++++++++++++++++++++++++ | 57% ~03s
|++++++++++++++++++++++++++++++ | 58% ~03s
|++++++++++++++++++++++++++++++ | 59% ~02s
|+++++++++++++++++++++++++++++++ | 60% ~02s
|+++++++++++++++++++++++++++++++ | 61% ~02s
|++++++++++++++++++++++++++++++++ | 62% ~02s
|++++++++++++++++++++++++++++++++ | 63% ~02s
|+++++++++++++++++++++++++++++++++ | 64% ~02s
|+++++++++++++++++++++++++++++++++ | 65% ~02s
|++++++++++++++++++++++++++++++++++ | 66% ~02s
|++++++++++++++++++++++++++++++++++ | 67% ~02s
|+++++++++++++++++++++++++++++++++++ | 68% ~02s
|+++++++++++++++++++++++++++++++++++ | 69% ~02s
|++++++++++++++++++++++++++++++++++++ | 70% ~02s
|++++++++++++++++++++++++++++++++++++ | 71% ~02s
|+++++++++++++++++++++++++++++++++++++ | 72% ~02s
|+++++++++++++++++++++++++++++++++++++ | 73% ~02s
|++++++++++++++++++++++++++++++++++++++ | 74% ~02s
|++++++++++++++++++++++++++++++++++++++ | 76% ~01s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~01s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~01s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~01s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~01s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~01s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=06s Calculating cluster 6
| | 0 % ~calculating
|+ | 1 % ~10s
|++ | 2 % ~10s
|++ | 3 % ~09s
|+++ | 4 % ~09s
|+++ | 5 % ~09s
|++++ | 6 % ~09s
|++++ | 7 % ~09s
|+++++ | 8 % ~09s
|+++++ | 9 % ~09s
|++++++ | 10% ~09s
|++++++ | 11% ~09s
|+++++++ | 12% ~09s
|+++++++ | 13% ~08s
|++++++++ | 14% ~08s
|++++++++ | 15% ~08s
|+++++++++ | 16% ~08s
|+++++++++ | 17% ~08s
|++++++++++ | 18% ~08s
|++++++++++ | 19% ~08s
|+++++++++++ | 20% ~08s
|+++++++++++ | 21% ~08s
|++++++++++++ | 22% ~08s
|++++++++++++ | 23% ~08s
|+++++++++++++ | 24% ~07s
|+++++++++++++ | 25% ~07s
|++++++++++++++ | 26% ~07s
|++++++++++++++ | 27% ~07s
|+++++++++++++++ | 28% ~07s
|+++++++++++++++ | 29% ~07s
|++++++++++++++++ | 30% ~07s
|++++++++++++++++ | 31% ~07s
|+++++++++++++++++ | 32% ~07s
|+++++++++++++++++ | 33% ~07s
|++++++++++++++++++ | 34% ~06s
|++++++++++++++++++ | 35% ~06s
|+++++++++++++++++++ | 36% ~06s
|+++++++++++++++++++ | 37% ~06s
|++++++++++++++++++++ | 38% ~06s
|++++++++++++++++++++ | 39% ~06s
|+++++++++++++++++++++ | 40% ~06s
|+++++++++++++++++++++ | 41% ~06s
|++++++++++++++++++++++ | 42% ~06s
|++++++++++++++++++++++ | 43% ~06s
|+++++++++++++++++++++++ | 44% ~05s
|+++++++++++++++++++++++ | 45% ~05s
|++++++++++++++++++++++++ | 46% ~05s
|++++++++++++++++++++++++ | 47% ~05s
|+++++++++++++++++++++++++ | 48% ~05s
|+++++++++++++++++++++++++ | 49% ~05s
|++++++++++++++++++++++++++ | 51% ~05s
|++++++++++++++++++++++++++ | 52% ~05s
|+++++++++++++++++++++++++++ | 53% ~05s
|+++++++++++++++++++++++++++ | 54% ~05s
|++++++++++++++++++++++++++++ | 55% ~04s
|++++++++++++++++++++++++++++ | 56% ~04s
|+++++++++++++++++++++++++++++ | 57% ~04s
|+++++++++++++++++++++++++++++ | 58% ~04s
|++++++++++++++++++++++++++++++ | 59% ~04s
|++++++++++++++++++++++++++++++ | 60% ~04s
|+++++++++++++++++++++++++++++++ | 61% ~04s
|+++++++++++++++++++++++++++++++ | 62% ~04s
|++++++++++++++++++++++++++++++++ | 63% ~04s
|++++++++++++++++++++++++++++++++ | 64% ~04s
|+++++++++++++++++++++++++++++++++ | 65% ~03s
|+++++++++++++++++++++++++++++++++ | 66% ~03s
|++++++++++++++++++++++++++++++++++ | 67% ~03s
|++++++++++++++++++++++++++++++++++ | 68% ~03s
|+++++++++++++++++++++++++++++++++++ | 69% ~03s
|+++++++++++++++++++++++++++++++++++ | 70% ~03s
|++++++++++++++++++++++++++++++++++++ | 71% ~03s
|++++++++++++++++++++++++++++++++++++ | 72% ~03s
|+++++++++++++++++++++++++++++++++++++ | 73% ~03s
|+++++++++++++++++++++++++++++++++++++ | 74% ~03s
|++++++++++++++++++++++++++++++++++++++ | 75% ~02s
|++++++++++++++++++++++++++++++++++++++ | 76% ~02s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~02s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~02s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~02s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~02s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~02s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~02s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~02s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~02s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=10s all_data.markers <- FindAllMarkers(all_data, only.pos = TRUE, min.pct = 0.25, logfc.threshold = 0.25) Calculating cluster 0
| | 0 % ~calculating
|+ | 1 % ~33s
|++ | 2 % ~33s
|++ | 3 % ~32s
|+++ | 4 % ~31s
|+++ | 5 % ~31s
|++++ | 6 % ~30s
|++++ | 7 % ~29s
|+++++ | 9 % ~28s
|+++++ | 10% ~28s
|++++++ | 11% ~28s
|++++++ | 12% ~27s
|+++++++ | 13% ~27s
|+++++++ | 14% ~27s
|++++++++ | 15% ~27s
|++++++++ | 16% ~26s
|+++++++++ | 17% ~26s
|++++++++++ | 18% ~26s
|++++++++++ | 19% ~26s
|+++++++++++ | 20% ~25s
|+++++++++++ | 21% ~28s
|++++++++++++ | 22% ~27s
|++++++++++++ | 23% ~27s
|+++++++++++++ | 24% ~26s
|+++++++++++++ | 26% ~26s
|++++++++++++++ | 27% ~26s
|++++++++++++++ | 28% ~25s
|+++++++++++++++ | 29% ~25s
|+++++++++++++++ | 30% ~24s
|++++++++++++++++ | 31% ~24s
|++++++++++++++++ | 32% ~23s
|+++++++++++++++++ | 33% ~23s
|++++++++++++++++++ | 34% ~22s
|++++++++++++++++++ | 35% ~22s
|+++++++++++++++++++ | 36% ~22s
|+++++++++++++++++++ | 37% ~21s
|++++++++++++++++++++ | 38% ~21s
|++++++++++++++++++++ | 39% ~20s
|+++++++++++++++++++++ | 40% ~20s
|+++++++++++++++++++++ | 41% ~20s
|++++++++++++++++++++++ | 43% ~19s
|++++++++++++++++++++++ | 44% ~19s
|+++++++++++++++++++++++ | 45% ~19s
|+++++++++++++++++++++++ | 46% ~18s
|++++++++++++++++++++++++ | 47% ~18s
|++++++++++++++++++++++++ | 48% ~18s
|+++++++++++++++++++++++++ | 49% ~17s
|+++++++++++++++++++++++++ | 50% ~17s
|++++++++++++++++++++++++++ | 51% ~16s
|+++++++++++++++++++++++++++ | 52% ~16s
|+++++++++++++++++++++++++++ | 53% ~16s
|++++++++++++++++++++++++++++ | 54% ~15s
|++++++++++++++++++++++++++++ | 55% ~15s
|+++++++++++++++++++++++++++++ | 56% ~15s
|+++++++++++++++++++++++++++++ | 57% ~14s
|++++++++++++++++++++++++++++++ | 59% ~14s
|++++++++++++++++++++++++++++++ | 60% ~14s
|+++++++++++++++++++++++++++++++ | 61% ~13s
|+++++++++++++++++++++++++++++++ | 62% ~13s
|++++++++++++++++++++++++++++++++ | 63% ~13s
|++++++++++++++++++++++++++++++++ | 64% ~12s
|+++++++++++++++++++++++++++++++++ | 65% ~12s
|+++++++++++++++++++++++++++++++++ | 66% ~12s
|++++++++++++++++++++++++++++++++++ | 67% ~11s
|+++++++++++++++++++++++++++++++++++ | 68% ~11s
|+++++++++++++++++++++++++++++++++++ | 69% ~10s
|++++++++++++++++++++++++++++++++++++ | 70% ~10s
|++++++++++++++++++++++++++++++++++++ | 71% ~10s
|+++++++++++++++++++++++++++++++++++++ | 72% ~09s
|+++++++++++++++++++++++++++++++++++++ | 73% ~09s
|++++++++++++++++++++++++++++++++++++++ | 74% ~09s
|++++++++++++++++++++++++++++++++++++++ | 76% ~08s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~08s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~08s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~07s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~07s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~07s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~06s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~06s
|+++++++++++++++++++++++++++++++++++++++++++ | 84% ~05s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~05s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~05s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~04s
|+++++++++++++++++++++++++++++++++++++++++++++ | 88% ~04s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~04s
|++++++++++++++++++++++++++++++++++++++++++++++ | 90% ~03s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~03s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~03s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=34s Calculating cluster 1
| | 0 % ~calculating
|+ | 1 % ~31s
|++ | 2 % ~29s
|++ | 3 % ~30s
|+++ | 4 % ~29s
|+++ | 5 % ~29s
|++++ | 7 % ~28s
|++++ | 8 % ~30s
|+++++ | 9 % ~29s
|+++++ | 10% ~29s
|++++++ | 11% ~28s
|+++++++ | 12% ~27s
|+++++++ | 13% ~27s
|++++++++ | 14% ~26s
|++++++++ | 15% ~26s
|+++++++++ | 16% ~26s
|+++++++++ | 18% ~25s
|++++++++++ | 19% ~25s
|++++++++++ | 20% ~24s
|+++++++++++ | 21% ~24s
|+++++++++++ | 22% ~24s
|++++++++++++ | 23% ~23s
|+++++++++++++ | 24% ~23s
|+++++++++++++ | 25% ~22s
|++++++++++++++ | 26% ~22s
|++++++++++++++ | 27% ~22s
|+++++++++++++++ | 29% ~21s
|+++++++++++++++ | 30% ~21s
|++++++++++++++++ | 31% ~21s
|++++++++++++++++ | 32% ~20s
|+++++++++++++++++ | 33% ~20s
|++++++++++++++++++ | 34% ~20s
|++++++++++++++++++ | 35% ~19s
|+++++++++++++++++++ | 36% ~19s
|+++++++++++++++++++ | 37% ~19s
|++++++++++++++++++++ | 38% ~18s
|++++++++++++++++++++ | 40% ~18s
|+++++++++++++++++++++ | 41% ~18s
|+++++++++++++++++++++ | 42% ~17s
|++++++++++++++++++++++ | 43% ~17s
|++++++++++++++++++++++ | 44% ~17s
|+++++++++++++++++++++++ | 45% ~17s
|++++++++++++++++++++++++ | 46% ~16s
|++++++++++++++++++++++++ | 47% ~16s
|+++++++++++++++++++++++++ | 48% ~16s
|+++++++++++++++++++++++++ | 49% ~15s
|++++++++++++++++++++++++++ | 51% ~15s
|++++++++++++++++++++++++++ | 52% ~15s
|+++++++++++++++++++++++++++ | 53% ~14s
|+++++++++++++++++++++++++++ | 54% ~14s
|++++++++++++++++++++++++++++ | 55% ~14s
|+++++++++++++++++++++++++++++ | 56% ~13s
|+++++++++++++++++++++++++++++ | 57% ~13s
|++++++++++++++++++++++++++++++ | 58% ~13s
|++++++++++++++++++++++++++++++ | 59% ~12s
|+++++++++++++++++++++++++++++++ | 60% ~12s
|+++++++++++++++++++++++++++++++ | 62% ~12s
|++++++++++++++++++++++++++++++++ | 63% ~11s
|++++++++++++++++++++++++++++++++ | 64% ~11s
|+++++++++++++++++++++++++++++++++ | 65% ~11s
|+++++++++++++++++++++++++++++++++ | 66% ~10s
|++++++++++++++++++++++++++++++++++ | 67% ~10s
|+++++++++++++++++++++++++++++++++++ | 68% ~10s
|+++++++++++++++++++++++++++++++++++ | 69% ~09s
|++++++++++++++++++++++++++++++++++++ | 70% ~09s
|++++++++++++++++++++++++++++++++++++ | 71% ~09s
|+++++++++++++++++++++++++++++++++++++ | 73% ~08s
|+++++++++++++++++++++++++++++++++++++ | 74% ~08s
|++++++++++++++++++++++++++++++++++++++ | 75% ~08s
|++++++++++++++++++++++++++++++++++++++ | 76% ~07s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~07s
|++++++++++++++++++++++++++++++++++++++++ | 78% ~07s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~06s
|+++++++++++++++++++++++++++++++++++++++++ | 80% ~06s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~06s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~05s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~05s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~05s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~04s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~04s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~04s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~03s
|++++++++++++++++++++++++++++++++++++++++++++++ | 90% ~03s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~03s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=31s Calculating cluster 2
| | 0 % ~calculating
|+ | 1 % ~01m 10s
|++ | 2 % ~01m 10s
|++ | 3 % ~01m 08s
|+++ | 5 % ~01m 08s
|+++ | 6 % ~01m 07s
|++++ | 7 % ~01m 06s
|+++++ | 8 % ~01m 05s
|+++++ | 9 % ~01m 04s
|++++++ | 10% ~01m 03s
|++++++ | 11% ~01m 02s
|+++++++ | 13% ~01m 02s
|+++++++ | 14% ~01m 01s
|++++++++ | 15% ~01m 01s
|+++++++++ | 16% ~01m 00s
|+++++++++ | 17% ~60s
|++++++++++ | 18% ~59s
|++++++++++ | 20% ~58s
|+++++++++++ | 21% ~57s
|+++++++++++ | 22% ~56s
|++++++++++++ | 23% ~56s
|+++++++++++++ | 24% ~55s
|+++++++++++++ | 25% ~54s
|++++++++++++++ | 26% ~54s
|++++++++++++++ | 28% ~53s
|+++++++++++++++ | 29% ~52s
|+++++++++++++++ | 30% ~51s
|++++++++++++++++ | 31% ~50s
|+++++++++++++++++ | 32% ~49s
|+++++++++++++++++ | 33% ~49s
|++++++++++++++++++ | 34% ~48s
|++++++++++++++++++ | 36% ~47s
|+++++++++++++++++++ | 37% ~46s
|+++++++++++++++++++ | 38% ~45s
|++++++++++++++++++++ | 39% ~45s
|+++++++++++++++++++++ | 40% ~44s
|+++++++++++++++++++++ | 41% ~43s
|++++++++++++++++++++++ | 43% ~43s
|++++++++++++++++++++++ | 44% ~42s
|+++++++++++++++++++++++ | 45% ~41s
|+++++++++++++++++++++++ | 46% ~40s
|++++++++++++++++++++++++ | 47% ~39s
|+++++++++++++++++++++++++ | 48% ~38s
|+++++++++++++++++++++++++ | 49% ~37s
|++++++++++++++++++++++++++ | 51% ~36s
|++++++++++++++++++++++++++ | 52% ~35s
|+++++++++++++++++++++++++++ | 53% ~35s
|++++++++++++++++++++++++++++ | 54% ~34s
|++++++++++++++++++++++++++++ | 55% ~33s
|+++++++++++++++++++++++++++++ | 56% ~32s
|+++++++++++++++++++++++++++++ | 57% ~31s
|++++++++++++++++++++++++++++++ | 59% ~30s
|++++++++++++++++++++++++++++++ | 60% ~29s
|+++++++++++++++++++++++++++++++ | 61% ~29s
|++++++++++++++++++++++++++++++++ | 62% ~28s
|++++++++++++++++++++++++++++++++ | 63% ~27s
|+++++++++++++++++++++++++++++++++ | 64% ~26s
|+++++++++++++++++++++++++++++++++ | 66% ~25s
|++++++++++++++++++++++++++++++++++ | 67% ~25s
|++++++++++++++++++++++++++++++++++ | 68% ~24s
|+++++++++++++++++++++++++++++++++++ | 69% ~23s
|++++++++++++++++++++++++++++++++++++ | 70% ~22s
|++++++++++++++++++++++++++++++++++++ | 71% ~21s
|+++++++++++++++++++++++++++++++++++++ | 72% ~20s
|+++++++++++++++++++++++++++++++++++++ | 74% ~19s
|++++++++++++++++++++++++++++++++++++++ | 75% ~19s
|++++++++++++++++++++++++++++++++++++++ | 76% ~18s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~17s
|++++++++++++++++++++++++++++++++++++++++ | 78% ~16s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~15s
|+++++++++++++++++++++++++++++++++++++++++ | 80% ~14s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~14s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~13s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~12s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~11s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~10s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~09s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~09s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~08s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~07s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~06s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~05s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~04s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~03s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~03s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=01m 14sCalculating cluster 3
| | 0 % ~calculating
|+ | 1 % ~01m 09s
|++ | 2 % ~01m 07s
|++ | 3 % ~01m 07s
|+++ | 4 % ~01m 05s
|+++ | 5 % ~01m 05s
|++++ | 6 % ~01m 03s
|++++ | 7 % ~01m 02s
|+++++ | 8 % ~01m 01s
|+++++ | 9 % ~01m 00s
|++++++ | 10% ~01m 06s
|++++++ | 11% ~01m 05s
|+++++++ | 12% ~01m 03s
|+++++++ | 14% ~01m 02s
|++++++++ | 15% ~01m 01s
|++++++++ | 16% ~60s
|+++++++++ | 17% ~59s
|+++++++++ | 18% ~58s
|++++++++++ | 19% ~57s
|++++++++++ | 20% ~56s
|+++++++++++ | 21% ~55s
|+++++++++++ | 22% ~55s
|++++++++++++ | 23% ~54s
|++++++++++++ | 24% ~53s
|+++++++++++++ | 25% ~52s
|++++++++++++++ | 26% ~51s
|++++++++++++++ | 27% ~51s
|+++++++++++++++ | 28% ~50s
|+++++++++++++++ | 29% ~49s
|++++++++++++++++ | 30% ~48s
|++++++++++++++++ | 31% ~48s
|+++++++++++++++++ | 32% ~47s
|+++++++++++++++++ | 33% ~46s
|++++++++++++++++++ | 34% ~46s
|++++++++++++++++++ | 35% ~45s
|+++++++++++++++++++ | 36% ~44s
|+++++++++++++++++++ | 38% ~43s
|++++++++++++++++++++ | 39% ~43s
|++++++++++++++++++++ | 40% ~42s
|+++++++++++++++++++++ | 41% ~41s
|+++++++++++++++++++++ | 42% ~41s
|++++++++++++++++++++++ | 43% ~40s
|++++++++++++++++++++++ | 44% ~39s
|+++++++++++++++++++++++ | 45% ~38s
|+++++++++++++++++++++++ | 46% ~38s
|++++++++++++++++++++++++ | 47% ~37s
|++++++++++++++++++++++++ | 48% ~36s
|+++++++++++++++++++++++++ | 49% ~35s
|+++++++++++++++++++++++++ | 50% ~35s
|++++++++++++++++++++++++++ | 51% ~34s
|+++++++++++++++++++++++++++ | 52% ~33s
|+++++++++++++++++++++++++++ | 53% ~33s
|++++++++++++++++++++++++++++ | 54% ~32s
|++++++++++++++++++++++++++++ | 55% ~31s
|+++++++++++++++++++++++++++++ | 56% ~31s
|+++++++++++++++++++++++++++++ | 57% ~30s
|++++++++++++++++++++++++++++++ | 58% ~29s
|++++++++++++++++++++++++++++++ | 59% ~28s
|+++++++++++++++++++++++++++++++ | 60% ~28s
|+++++++++++++++++++++++++++++++ | 61% ~27s
|++++++++++++++++++++++++++++++++ | 62% ~26s
|++++++++++++++++++++++++++++++++ | 64% ~25s
|+++++++++++++++++++++++++++++++++ | 65% ~25s
|+++++++++++++++++++++++++++++++++ | 66% ~24s
|++++++++++++++++++++++++++++++++++ | 67% ~23s
|++++++++++++++++++++++++++++++++++ | 68% ~22s
|+++++++++++++++++++++++++++++++++++ | 69% ~22s
|+++++++++++++++++++++++++++++++++++ | 70% ~21s
|++++++++++++++++++++++++++++++++++++ | 71% ~20s
|++++++++++++++++++++++++++++++++++++ | 72% ~19s
|+++++++++++++++++++++++++++++++++++++ | 73% ~19s
|+++++++++++++++++++++++++++++++++++++ | 74% ~18s
|++++++++++++++++++++++++++++++++++++++ | 75% ~17s
|+++++++++++++++++++++++++++++++++++++++ | 76% ~16s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~16s
|++++++++++++++++++++++++++++++++++++++++ | 78% ~15s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~14s
|+++++++++++++++++++++++++++++++++++++++++ | 80% ~14s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~13s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~12s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~11s
|+++++++++++++++++++++++++++++++++++++++++++ | 84% ~11s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~10s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~09s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~09s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~08s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~07s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~06s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~06s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~05s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~04s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~04s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~03s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=01m 09sCalculating cluster 4
| | 0 % ~calculating
|+ | 1 % ~01m 45s
|++ | 2 % ~01m 43s
|++ | 3 % ~01m 41s
|+++ | 4 % ~01m 40s
|+++ | 5 % ~01m 37s
|++++ | 6 % ~01m 34s
|++++ | 7 % ~01m 33s
|+++++ | 8 % ~01m 32s
|+++++ | 9 % ~01m 31s
|++++++ | 10% ~01m 30s
|++++++ | 11% ~01m 30s
|+++++++ | 12% ~01m 29s
|+++++++ | 13% ~01m 28s
|++++++++ | 14% ~01m 27s
|++++++++ | 15% ~01m 27s
|+++++++++ | 16% ~01m 26s
|+++++++++ | 17% ~01m 25s
|++++++++++ | 18% ~01m 24s
|++++++++++ | 19% ~01m 23s
|+++++++++++ | 20% ~01m 22s
|+++++++++++ | 21% ~01m 21s
|++++++++++++ | 22% ~01m 20s
|++++++++++++ | 23% ~01m 20s
|+++++++++++++ | 24% ~01m 18s
|+++++++++++++ | 26% ~01m 17s
|++++++++++++++ | 27% ~01m 16s
|++++++++++++++ | 28% ~01m 15s
|+++++++++++++++ | 29% ~01m 14s
|+++++++++++++++ | 30% ~01m 14s
|++++++++++++++++ | 31% ~01m 13s
|++++++++++++++++ | 32% ~01m 12s
|+++++++++++++++++ | 33% ~01m 10s
|+++++++++++++++++ | 34% ~01m 09s
|++++++++++++++++++ | 35% ~01m 08s
|++++++++++++++++++ | 36% ~01m 07s
|+++++++++++++++++++ | 37% ~01m 06s
|+++++++++++++++++++ | 38% ~01m 05s
|++++++++++++++++++++ | 39% ~01m 03s
|++++++++++++++++++++ | 40% ~01m 02s
|+++++++++++++++++++++ | 41% ~01m 01s
|+++++++++++++++++++++ | 42% ~01m 00s
|++++++++++++++++++++++ | 43% ~59s
|++++++++++++++++++++++ | 44% ~58s
|+++++++++++++++++++++++ | 45% ~57s
|+++++++++++++++++++++++ | 46% ~56s
|++++++++++++++++++++++++ | 47% ~55s
|++++++++++++++++++++++++ | 48% ~54s
|+++++++++++++++++++++++++ | 49% ~53s
|+++++++++++++++++++++++++ | 50% ~52s
|++++++++++++++++++++++++++ | 51% ~51s
|+++++++++++++++++++++++++++ | 52% ~50s
|+++++++++++++++++++++++++++ | 53% ~49s
|++++++++++++++++++++++++++++ | 54% ~48s
|++++++++++++++++++++++++++++ | 55% ~47s
|+++++++++++++++++++++++++++++ | 56% ~46s
|+++++++++++++++++++++++++++++ | 57% ~45s
|++++++++++++++++++++++++++++++ | 58% ~44s
|++++++++++++++++++++++++++++++ | 59% ~43s
|+++++++++++++++++++++++++++++++ | 60% ~42s
|+++++++++++++++++++++++++++++++ | 61% ~41s
|++++++++++++++++++++++++++++++++ | 62% ~40s
|++++++++++++++++++++++++++++++++ | 63% ~39s
|+++++++++++++++++++++++++++++++++ | 64% ~38s
|+++++++++++++++++++++++++++++++++ | 65% ~37s
|++++++++++++++++++++++++++++++++++ | 66% ~35s
|++++++++++++++++++++++++++++++++++ | 67% ~34s
|+++++++++++++++++++++++++++++++++++ | 68% ~33s
|+++++++++++++++++++++++++++++++++++ | 69% ~32s
|++++++++++++++++++++++++++++++++++++ | 70% ~31s
|++++++++++++++++++++++++++++++++++++ | 71% ~30s
|+++++++++++++++++++++++++++++++++++++ | 72% ~29s
|+++++++++++++++++++++++++++++++++++++ | 73% ~28s
|++++++++++++++++++++++++++++++++++++++ | 74% ~27s
|++++++++++++++++++++++++++++++++++++++ | 76% ~26s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~25s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~24s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~22s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~21s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~20s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~19s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~18s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~17s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~16s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~15s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~14s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~13s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~12s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~11s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~10s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~09s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~08s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~06s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~05s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~04s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~03s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=01m 45sCalculating cluster 5
| | 0 % ~calculating
|+ | 1 % ~30s
|++ | 3 % ~30s
|++ | 4 % ~29s
|+++ | 5 % ~29s
|++++ | 7 % ~28s
|++++ | 8 % ~28s
|+++++ | 9 % ~28s
|++++++ | 11% ~27s
|++++++ | 12% ~27s
|+++++++ | 13% ~27s
|++++++++ | 14% ~26s
|++++++++ | 16% ~26s
|+++++++++ | 17% ~25s
|++++++++++ | 18% ~25s
|++++++++++ | 20% ~25s
|+++++++++++ | 21% ~24s
|++++++++++++ | 22% ~24s
|++++++++++++ | 24% ~24s
|+++++++++++++ | 25% ~23s
|++++++++++++++ | 26% ~23s
|++++++++++++++ | 28% ~23s
|+++++++++++++++ | 29% ~22s
|++++++++++++++++ | 30% ~22s
|++++++++++++++++ | 32% ~21s
|+++++++++++++++++ | 33% ~21s
|++++++++++++++++++ | 34% ~21s
|++++++++++++++++++ | 36% ~20s
|+++++++++++++++++++ | 37% ~20s
|++++++++++++++++++++ | 38% ~20s
|++++++++++++++++++++ | 39% ~19s
|+++++++++++++++++++++ | 41% ~19s
|++++++++++++++++++++++ | 42% ~19s
|++++++++++++++++++++++ | 43% ~18s
|+++++++++++++++++++++++ | 45% ~18s
|++++++++++++++++++++++++ | 46% ~17s
|++++++++++++++++++++++++ | 47% ~17s
|+++++++++++++++++++++++++ | 49% ~17s
|+++++++++++++++++++++++++ | 50% ~16s
|++++++++++++++++++++++++++ | 51% ~16s
|+++++++++++++++++++++++++++ | 53% ~16s
|+++++++++++++++++++++++++++ | 54% ~16s
|++++++++++++++++++++++++++++ | 55% ~15s
|+++++++++++++++++++++++++++++ | 57% ~15s
|+++++++++++++++++++++++++++++ | 58% ~14s
|++++++++++++++++++++++++++++++ | 59% ~14s
|+++++++++++++++++++++++++++++++ | 61% ~13s
|+++++++++++++++++++++++++++++++ | 62% ~13s
|++++++++++++++++++++++++++++++++ | 63% ~13s
|+++++++++++++++++++++++++++++++++ | 64% ~12s
|+++++++++++++++++++++++++++++++++ | 66% ~12s
|++++++++++++++++++++++++++++++++++ | 67% ~11s
|+++++++++++++++++++++++++++++++++++ | 68% ~11s
|+++++++++++++++++++++++++++++++++++ | 70% ~10s
|++++++++++++++++++++++++++++++++++++ | 71% ~10s
|+++++++++++++++++++++++++++++++++++++ | 72% ~09s
|+++++++++++++++++++++++++++++++++++++ | 74% ~09s
|++++++++++++++++++++++++++++++++++++++ | 75% ~08s
|+++++++++++++++++++++++++++++++++++++++ | 76% ~08s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~08s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~07s
|+++++++++++++++++++++++++++++++++++++++++ | 80% ~07s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~06s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~06s
|+++++++++++++++++++++++++++++++++++++++++++ | 84% ~05s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~05s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~04s
|+++++++++++++++++++++++++++++++++++++++++++++ | 88% ~04s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~04s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~03s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~03s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=34s Calculating cluster 6
| | 0 % ~calculating
|+ | 1 % ~01m 04s
|++ | 2 % ~01m 02s
|++ | 4 % ~01m 01s
|+++ | 5 % ~01m 01s
|+++ | 6 % ~60s
|++++ | 7 % ~59s
|+++++ | 8 % ~58s
|+++++ | 10% ~01m 00s
|++++++ | 11% ~59s
|++++++ | 12% ~58s
|+++++++ | 13% ~57s
|++++++++ | 14% ~56s
|++++++++ | 15% ~55s
|+++++++++ | 17% ~54s
|+++++++++ | 18% ~53s
|++++++++++ | 19% ~52s
|+++++++++++ | 20% ~52s
|+++++++++++ | 21% ~51s
|++++++++++++ | 23% ~50s
|++++++++++++ | 24% ~50s
|+++++++++++++ | 25% ~49s
|++++++++++++++ | 26% ~48s
|++++++++++++++ | 27% ~47s
|+++++++++++++++ | 29% ~47s
|+++++++++++++++ | 30% ~46s
|++++++++++++++++ | 31% ~45s
|+++++++++++++++++ | 32% ~44s
|+++++++++++++++++ | 33% ~44s
|++++++++++++++++++ | 35% ~43s
|++++++++++++++++++ | 36% ~42s
|+++++++++++++++++++ | 37% ~41s
|++++++++++++++++++++ | 38% ~41s
|++++++++++++++++++++ | 39% ~40s
|+++++++++++++++++++++ | 40% ~39s
|+++++++++++++++++++++ | 42% ~38s
|++++++++++++++++++++++ | 43% ~38s
|+++++++++++++++++++++++ | 44% ~37s
|+++++++++++++++++++++++ | 45% ~36s
|++++++++++++++++++++++++ | 46% ~35s
|++++++++++++++++++++++++ | 48% ~35s
|+++++++++++++++++++++++++ | 49% ~34s
|+++++++++++++++++++++++++ | 50% ~33s
|++++++++++++++++++++++++++ | 51% ~33s
|+++++++++++++++++++++++++++ | 52% ~32s
|+++++++++++++++++++++++++++ | 54% ~31s
|++++++++++++++++++++++++++++ | 55% ~30s
|++++++++++++++++++++++++++++ | 56% ~29s
|+++++++++++++++++++++++++++++ | 57% ~29s
|++++++++++++++++++++++++++++++ | 58% ~28s
|++++++++++++++++++++++++++++++ | 60% ~27s
|+++++++++++++++++++++++++++++++ | 61% ~26s
|+++++++++++++++++++++++++++++++ | 62% ~25s
|++++++++++++++++++++++++++++++++ | 63% ~24s
|+++++++++++++++++++++++++++++++++ | 64% ~24s
|+++++++++++++++++++++++++++++++++ | 65% ~23s
|++++++++++++++++++++++++++++++++++ | 67% ~22s
|++++++++++++++++++++++++++++++++++ | 68% ~21s
|+++++++++++++++++++++++++++++++++++ | 69% ~20s
|++++++++++++++++++++++++++++++++++++ | 70% ~20s
|++++++++++++++++++++++++++++++++++++ | 71% ~19s
|+++++++++++++++++++++++++++++++++++++ | 73% ~18s
|+++++++++++++++++++++++++++++++++++++ | 74% ~17s
|++++++++++++++++++++++++++++++++++++++ | 75% ~17s
|+++++++++++++++++++++++++++++++++++++++ | 76% ~16s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~15s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~14s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~13s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~13s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~12s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~11s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~10s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~10s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~09s
|+++++++++++++++++++++++++++++++++++++++++++++ | 88% ~08s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~07s
|++++++++++++++++++++++++++++++++++++++++++++++ | 90% ~06s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~06s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~05s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~04s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~03s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=01m 08sCalculating cluster 7
| | 0 % ~calculating
|+ | 1 % ~14s
|++ | 3 % ~14s
|++ | 4 % ~14s
|+++ | 5 % ~14s
|++++ | 7 % ~13s
|++++ | 8 % ~13s
|+++++ | 9 % ~13s
|++++++ | 11% ~12s
|++++++ | 12% ~12s
|+++++++ | 13% ~12s
|++++++++ | 15% ~12s
|++++++++ | 16% ~12s
|+++++++++ | 17% ~12s
|++++++++++ | 19% ~11s
|++++++++++ | 20% ~11s
|+++++++++++ | 21% ~11s
|++++++++++++ | 23% ~11s
|++++++++++++ | 24% ~11s
|+++++++++++++ | 25% ~10s
|++++++++++++++ | 27% ~10s
|++++++++++++++ | 28% ~10s
|+++++++++++++++ | 29% ~10s
|++++++++++++++++ | 31% ~10s
|++++++++++++++++ | 32% ~09s
|+++++++++++++++++ | 33% ~09s
|++++++++++++++++++ | 35% ~09s
|++++++++++++++++++ | 36% ~09s
|+++++++++++++++++++ | 37% ~09s
|++++++++++++++++++++ | 39% ~09s
|++++++++++++++++++++ | 40% ~08s
|+++++++++++++++++++++ | 41% ~08s
|++++++++++++++++++++++ | 43% ~08s
|++++++++++++++++++++++ | 44% ~08s
|+++++++++++++++++++++++ | 45% ~08s
|++++++++++++++++++++++++ | 47% ~07s
|++++++++++++++++++++++++ | 48% ~07s
|+++++++++++++++++++++++++ | 49% ~07s
|++++++++++++++++++++++++++ | 51% ~07s
|++++++++++++++++++++++++++ | 52% ~07s
|+++++++++++++++++++++++++++ | 53% ~07s
|++++++++++++++++++++++++++++ | 55% ~06s
|++++++++++++++++++++++++++++ | 56% ~06s
|+++++++++++++++++++++++++++++ | 57% ~06s
|++++++++++++++++++++++++++++++ | 59% ~06s
|++++++++++++++++++++++++++++++ | 60% ~06s
|+++++++++++++++++++++++++++++++ | 61% ~05s
|++++++++++++++++++++++++++++++++ | 63% ~05s
|++++++++++++++++++++++++++++++++ | 64% ~05s
|+++++++++++++++++++++++++++++++++ | 65% ~05s
|++++++++++++++++++++++++++++++++++ | 67% ~05s
|++++++++++++++++++++++++++++++++++ | 68% ~05s
|+++++++++++++++++++++++++++++++++++ | 69% ~04s
|++++++++++++++++++++++++++++++++++++ | 71% ~04s
|++++++++++++++++++++++++++++++++++++ | 72% ~04s
|+++++++++++++++++++++++++++++++++++++ | 73% ~04s
|++++++++++++++++++++++++++++++++++++++ | 75% ~04s
|++++++++++++++++++++++++++++++++++++++ | 76% ~03s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~03s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~03s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~03s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~03s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~03s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~02s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~02s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~02s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=15s Calculating cluster 8
| | 0 % ~calculating
|+ | 1 % ~01m 38s
|++ | 2 % ~01m 39s
|++ | 3 % ~01m 37s
|+++ | 4 % ~01m 42s
|+++ | 5 % ~01m 38s
|++++ | 6 % ~01m 36s
|++++ | 7 % ~01m 35s
|+++++ | 8 % ~01m 34s
|+++++ | 9 % ~01m 32s
|++++++ | 10% ~01m 31s
|++++++ | 11% ~01m 31s
|+++++++ | 12% ~01m 30s
|+++++++ | 14% ~01m 29s
|++++++++ | 15% ~01m 29s
|++++++++ | 16% ~01m 27s
|+++++++++ | 17% ~01m 26s
|+++++++++ | 18% ~01m 25s
|++++++++++ | 19% ~01m 24s
|++++++++++ | 20% ~01m 23s
|+++++++++++ | 21% ~01m 22s
|+++++++++++ | 22% ~01m 21s
|++++++++++++ | 23% ~01m 20s
|++++++++++++ | 24% ~01m 19s
|+++++++++++++ | 25% ~01m 18s
|++++++++++++++ | 26% ~01m 17s
|++++++++++++++ | 27% ~01m 17s
|+++++++++++++++ | 28% ~01m 15s
|+++++++++++++++ | 29% ~01m 14s
|++++++++++++++++ | 30% ~01m 13s
|++++++++++++++++ | 31% ~01m 11s
|+++++++++++++++++ | 32% ~01m 10s
|+++++++++++++++++ | 33% ~01m 09s
|++++++++++++++++++ | 34% ~01m 07s
|++++++++++++++++++ | 35% ~01m 06s
|+++++++++++++++++++ | 36% ~01m 05s
|+++++++++++++++++++ | 38% ~01m 04s
|++++++++++++++++++++ | 39% ~01m 03s
|++++++++++++++++++++ | 40% ~01m 02s
|+++++++++++++++++++++ | 41% ~01m 01s
|+++++++++++++++++++++ | 42% ~01m 00s
|++++++++++++++++++++++ | 43% ~59s
|++++++++++++++++++++++ | 44% ~58s
|+++++++++++++++++++++++ | 45% ~57s
|+++++++++++++++++++++++ | 46% ~56s
|++++++++++++++++++++++++ | 47% ~55s
|++++++++++++++++++++++++ | 48% ~54s
|+++++++++++++++++++++++++ | 49% ~53s
|+++++++++++++++++++++++++ | 50% ~52s
|++++++++++++++++++++++++++ | 51% ~51s
|+++++++++++++++++++++++++++ | 52% ~50s
|+++++++++++++++++++++++++++ | 53% ~49s
|++++++++++++++++++++++++++++ | 54% ~47s
|++++++++++++++++++++++++++++ | 55% ~46s
|+++++++++++++++++++++++++++++ | 56% ~45s
|+++++++++++++++++++++++++++++ | 57% ~44s
|++++++++++++++++++++++++++++++ | 58% ~43s
|++++++++++++++++++++++++++++++ | 59% ~42s
|+++++++++++++++++++++++++++++++ | 60% ~41s
|+++++++++++++++++++++++++++++++ | 61% ~40s
|++++++++++++++++++++++++++++++++ | 62% ~39s
|++++++++++++++++++++++++++++++++ | 64% ~38s
|+++++++++++++++++++++++++++++++++ | 65% ~37s
|+++++++++++++++++++++++++++++++++ | 66% ~35s
|++++++++++++++++++++++++++++++++++ | 67% ~34s
|++++++++++++++++++++++++++++++++++ | 68% ~33s
|+++++++++++++++++++++++++++++++++++ | 69% ~32s
|+++++++++++++++++++++++++++++++++++ | 70% ~31s
|++++++++++++++++++++++++++++++++++++ | 71% ~30s
|++++++++++++++++++++++++++++++++++++ | 72% ~29s
|+++++++++++++++++++++++++++++++++++++ | 73% ~28s
|+++++++++++++++++++++++++++++++++++++ | 74% ~27s
|++++++++++++++++++++++++++++++++++++++ | 75% ~26s
|+++++++++++++++++++++++++++++++++++++++ | 76% ~25s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~24s
|++++++++++++++++++++++++++++++++++++++++ | 78% ~23s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~22s
|+++++++++++++++++++++++++++++++++++++++++ | 80% ~20s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~19s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~18s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~17s
|+++++++++++++++++++++++++++++++++++++++++++ | 84% ~16s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~15s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~14s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~13s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~12s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~11s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~10s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~09s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~08s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~06s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~05s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~04s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~03s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=01m 44sCalculating cluster 9
| | 0 % ~calculating
|+ | 1 % ~02m 18s
|++ | 2 % ~02m 18s
|++ | 3 % ~02m 14s
|+++ | 4 % ~02m 12s
|+++ | 5 % ~02m 11s
|++++ | 6 % ~02m 10s
|++++ | 7 % ~02m 09s
|+++++ | 9 % ~02m 09s
|+++++ | 10% ~02m 08s
|++++++ | 11% ~02m 07s
|++++++ | 12% ~02m 06s
|+++++++ | 13% ~02m 05s
|+++++++ | 14% ~02m 03s
|++++++++ | 15% ~02m 03s
|++++++++ | 16% ~02m 01s
|+++++++++ | 17% ~01m 59s
|++++++++++ | 18% ~01m 58s
|++++++++++ | 19% ~01m 56s
|+++++++++++ | 20% ~01m 54s
|+++++++++++ | 21% ~01m 52s
|++++++++++++ | 22% ~01m 51s
|++++++++++++ | 23% ~01m 49s
|+++++++++++++ | 24% ~01m 48s
|+++++++++++++ | 26% ~01m 46s
|++++++++++++++ | 27% ~01m 45s
|++++++++++++++ | 28% ~01m 43s
|+++++++++++++++ | 29% ~01m 42s
|+++++++++++++++ | 30% ~01m 40s
|++++++++++++++++ | 31% ~01m 39s
|++++++++++++++++ | 32% ~01m 38s
|+++++++++++++++++ | 33% ~01m 36s
|++++++++++++++++++ | 34% ~01m 34s
|++++++++++++++++++ | 35% ~01m 33s
|+++++++++++++++++++ | 36% ~01m 31s
|+++++++++++++++++++ | 37% ~01m 29s
|++++++++++++++++++++ | 38% ~01m 28s
|++++++++++++++++++++ | 39% ~01m 26s
|+++++++++++++++++++++ | 40% ~01m 25s
|+++++++++++++++++++++ | 41% ~01m 23s
|++++++++++++++++++++++ | 43% ~01m 22s
|++++++++++++++++++++++ | 44% ~01m 21s
|+++++++++++++++++++++++ | 45% ~01m 19s
|+++++++++++++++++++++++ | 46% ~01m 18s
|++++++++++++++++++++++++ | 47% ~01m 17s
|++++++++++++++++++++++++ | 48% ~01m 15s
|+++++++++++++++++++++++++ | 49% ~01m 13s
|+++++++++++++++++++++++++ | 50% ~01m 12s
|++++++++++++++++++++++++++ | 51% ~01m 10s
|+++++++++++++++++++++++++++ | 52% ~01m 08s
|+++++++++++++++++++++++++++ | 53% ~01m 07s
|++++++++++++++++++++++++++++ | 54% ~01m 05s
|++++++++++++++++++++++++++++ | 55% ~01m 04s
|+++++++++++++++++++++++++++++ | 56% ~01m 02s
|+++++++++++++++++++++++++++++ | 57% ~01m 01s
|++++++++++++++++++++++++++++++ | 59% ~59s
|++++++++++++++++++++++++++++++ | 60% ~58s
|+++++++++++++++++++++++++++++++ | 61% ~56s
|+++++++++++++++++++++++++++++++ | 62% ~55s
|++++++++++++++++++++++++++++++++ | 63% ~53s
|++++++++++++++++++++++++++++++++ | 64% ~52s
|+++++++++++++++++++++++++++++++++ | 65% ~50s
|+++++++++++++++++++++++++++++++++ | 66% ~49s
|++++++++++++++++++++++++++++++++++ | 67% ~47s
|+++++++++++++++++++++++++++++++++++ | 68% ~46s
|+++++++++++++++++++++++++++++++++++ | 69% ~44s
|++++++++++++++++++++++++++++++++++++ | 70% ~43s
|++++++++++++++++++++++++++++++++++++ | 71% ~41s
|+++++++++++++++++++++++++++++++++++++ | 72% ~39s
|+++++++++++++++++++++++++++++++++++++ | 73% ~38s
|++++++++++++++++++++++++++++++++++++++ | 74% ~36s
|++++++++++++++++++++++++++++++++++++++ | 76% ~35s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~33s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~32s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~30s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~29s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~27s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~26s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~24s
|+++++++++++++++++++++++++++++++++++++++++++ | 84% ~23s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~21s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~20s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~18s
|+++++++++++++++++++++++++++++++++++++++++++++ | 88% ~17s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~15s
|++++++++++++++++++++++++++++++++++++++++++++++ | 90% ~14s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~12s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~11s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~09s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~08s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~06s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~05s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~03s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=02m 22sCalculating cluster 10
| | 0 % ~calculating
|+ | 1 % ~45s
|++ | 2 % ~46s
|++ | 3 % ~45s
|+++ | 4 % ~45s
|+++ | 5 % ~44s
|++++ | 7 % ~43s
|++++ | 8 % ~43s
|+++++ | 9 % ~42s
|+++++ | 10% ~41s
|++++++ | 11% ~41s
|++++++ | 12% ~40s
|+++++++ | 13% ~40s
|++++++++ | 14% ~40s
|++++++++ | 15% ~39s
|+++++++++ | 16% ~39s
|+++++++++ | 17% ~39s
|++++++++++ | 18% ~38s
|++++++++++ | 20% ~38s
|+++++++++++ | 21% ~38s
|+++++++++++ | 22% ~37s
|++++++++++++ | 23% ~37s
|++++++++++++ | 24% ~36s
|+++++++++++++ | 25% ~36s
|++++++++++++++ | 26% ~36s
|++++++++++++++ | 27% ~35s
|+++++++++++++++ | 28% ~35s
|+++++++++++++++ | 29% ~34s
|++++++++++++++++ | 30% ~34s
|++++++++++++++++ | 32% ~33s
|+++++++++++++++++ | 33% ~33s
|+++++++++++++++++ | 34% ~32s
|++++++++++++++++++ | 35% ~32s
|++++++++++++++++++ | 36% ~31s
|+++++++++++++++++++ | 37% ~31s
|++++++++++++++++++++ | 38% ~31s
|++++++++++++++++++++ | 39% ~30s
|+++++++++++++++++++++ | 40% ~30s
|+++++++++++++++++++++ | 41% ~29s
|++++++++++++++++++++++ | 42% ~28s
|++++++++++++++++++++++ | 43% ~28s
|+++++++++++++++++++++++ | 45% ~27s
|+++++++++++++++++++++++ | 46% ~27s
|++++++++++++++++++++++++ | 47% ~26s
|++++++++++++++++++++++++ | 48% ~26s
|+++++++++++++++++++++++++ | 49% ~25s
|+++++++++++++++++++++++++ | 50% ~24s
|++++++++++++++++++++++++++ | 51% ~24s
|+++++++++++++++++++++++++++ | 52% ~23s
|+++++++++++++++++++++++++++ | 53% ~23s
|++++++++++++++++++++++++++++ | 54% ~22s
|++++++++++++++++++++++++++++ | 55% ~22s
|+++++++++++++++++++++++++++++ | 57% ~21s
|+++++++++++++++++++++++++++++ | 58% ~21s
|++++++++++++++++++++++++++++++ | 59% ~20s
|++++++++++++++++++++++++++++++ | 60% ~20s
|+++++++++++++++++++++++++++++++ | 61% ~19s
|+++++++++++++++++++++++++++++++ | 62% ~19s
|++++++++++++++++++++++++++++++++ | 63% ~18s
|+++++++++++++++++++++++++++++++++ | 64% ~18s
|+++++++++++++++++++++++++++++++++ | 65% ~17s
|++++++++++++++++++++++++++++++++++ | 66% ~16s
|++++++++++++++++++++++++++++++++++ | 67% ~16s
|+++++++++++++++++++++++++++++++++++ | 68% ~15s
|+++++++++++++++++++++++++++++++++++ | 70% ~15s
|++++++++++++++++++++++++++++++++++++ | 71% ~14s
|++++++++++++++++++++++++++++++++++++ | 72% ~14s
|+++++++++++++++++++++++++++++++++++++ | 73% ~13s
|+++++++++++++++++++++++++++++++++++++ | 74% ~13s
|++++++++++++++++++++++++++++++++++++++ | 75% ~12s
|+++++++++++++++++++++++++++++++++++++++ | 76% ~12s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~11s
|++++++++++++++++++++++++++++++++++++++++ | 78% ~11s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~10s
|+++++++++++++++++++++++++++++++++++++++++ | 80% ~10s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~09s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~09s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~08s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~08s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~07s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~06s
|+++++++++++++++++++++++++++++++++++++++++++++ | 88% ~06s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~05s
|++++++++++++++++++++++++++++++++++++++++++++++ | 90% ~05s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~04s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~04s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~03s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~03s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=50s Calculating cluster 11
| | 0 % ~calculating
|+ | 1 % ~02m 58s
|++ | 2 % ~02m 58s
|++ | 3 % ~03m 01s
|+++ | 4 % ~02m 55s
|+++ | 5 % ~02m 50s
|++++ | 6 % ~02m 48s
|++++ | 7 % ~02m 46s
|+++++ | 8 % ~02m 45s
|+++++ | 9 % ~02m 43s
|++++++ | 11% ~02m 42s
|++++++ | 12% ~02m 40s
|+++++++ | 13% ~02m 39s
|+++++++ | 14% ~02m 37s
|++++++++ | 15% ~02m 36s
|++++++++ | 16% ~02m 34s
|+++++++++ | 17% ~02m 34s
|+++++++++ | 18% ~02m 32s
|++++++++++ | 19% ~02m 29s
|++++++++++ | 20% ~02m 27s
|+++++++++++ | 21% ~02m 24s
|++++++++++++ | 22% ~02m 22s
|++++++++++++ | 23% ~02m 20s
|+++++++++++++ | 24% ~02m 18s
|+++++++++++++ | 25% ~02m 16s
|++++++++++++++ | 26% ~02m 14s
|++++++++++++++ | 27% ~02m 12s
|+++++++++++++++ | 28% ~02m 10s
|+++++++++++++++ | 29% ~02m 09s
|++++++++++++++++ | 31% ~02m 07s
|++++++++++++++++ | 32% ~02m 04s
|+++++++++++++++++ | 33% ~02m 02s
|+++++++++++++++++ | 34% ~02m 00s
|++++++++++++++++++ | 35% ~01m 58s
|++++++++++++++++++ | 36% ~01m 56s
|+++++++++++++++++++ | 37% ~01m 54s
|+++++++++++++++++++ | 38% ~01m 52s
|++++++++++++++++++++ | 39% ~01m 50s
|++++++++++++++++++++ | 40% ~01m 49s
|+++++++++++++++++++++ | 41% ~01m 47s
|++++++++++++++++++++++ | 42% ~01m 45s
|++++++++++++++++++++++ | 43% ~01m 44s
|+++++++++++++++++++++++ | 44% ~01m 41s
|+++++++++++++++++++++++ | 45% ~01m 39s
|++++++++++++++++++++++++ | 46% ~01m 37s
|++++++++++++++++++++++++ | 47% ~01m 35s
|+++++++++++++++++++++++++ | 48% ~01m 33s
|+++++++++++++++++++++++++ | 49% ~01m 31s
|++++++++++++++++++++++++++ | 51% ~01m 29s
|++++++++++++++++++++++++++ | 52% ~01m 27s
|+++++++++++++++++++++++++++ | 53% ~01m 26s
|+++++++++++++++++++++++++++ | 54% ~01m 24s
|++++++++++++++++++++++++++++ | 55% ~01m 22s
|++++++++++++++++++++++++++++ | 56% ~01m 20s
|+++++++++++++++++++++++++++++ | 57% ~01m 18s
|+++++++++++++++++++++++++++++ | 58% ~01m 16s
|++++++++++++++++++++++++++++++ | 59% ~01m 14s
|++++++++++++++++++++++++++++++ | 60% ~01m 12s
|+++++++++++++++++++++++++++++++ | 61% ~01m 10s
|++++++++++++++++++++++++++++++++ | 62% ~01m 08s
|++++++++++++++++++++++++++++++++ | 63% ~01m 07s
|+++++++++++++++++++++++++++++++++ | 64% ~01m 05s
|+++++++++++++++++++++++++++++++++ | 65% ~01m 03s
|++++++++++++++++++++++++++++++++++ | 66% ~01m 01s
|++++++++++++++++++++++++++++++++++ | 67% ~59s
|+++++++++++++++++++++++++++++++++++ | 68% ~57s
|+++++++++++++++++++++++++++++++++++ | 69% ~55s
|++++++++++++++++++++++++++++++++++++ | 71% ~53s
|++++++++++++++++++++++++++++++++++++ | 72% ~51s
|+++++++++++++++++++++++++++++++++++++ | 73% ~49s
|+++++++++++++++++++++++++++++++++++++ | 74% ~47s
|++++++++++++++++++++++++++++++++++++++ | 75% ~46s
|++++++++++++++++++++++++++++++++++++++ | 76% ~44s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~42s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~40s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~38s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~36s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~34s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~32s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~30s
|+++++++++++++++++++++++++++++++++++++++++++ | 84% ~28s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~27s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~25s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~23s
|+++++++++++++++++++++++++++++++++++++++++++++ | 88% ~21s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~19s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~17s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~15s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~13s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~11s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~09s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~08s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~06s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~04s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=03m 00ssave(all_data.markers, cis_markers, cocl2_markers, dabtram_markers, file = 'all_data_markers.RData')#find lineages that are maintained at both dabtram timepoints
fivecell_cDNA$DabTramMaintained <- Reduce(intersect, list(fivecell_cDNA$DabTram, fivecell_cDNA$DabTramtoDabTram))
filtered_meta <- rep(0, length(names(all_data$Lineage)))
#specify which cells are in lineages that pass filtering for that condition
filtered_meta[which(all_data$OG_condition == "dabtram" & all_data$Lineage %in% combined_lins_list$DabTram)] <- 'Resistant to DabTram'
filtered_meta[which(all_data$OG_condition == "dabtramtodabtram" & all_data$Lineage %in% combined_lins_list$DabTramtoDabTram)] <- 'Resistant to DabTramtoDabTram'
filtered_meta[which(all_data$OG_condition == "dabtramtococl2" & all_data$Lineage %in% combined_lins_list$DabTramtoCoCl2)] <- 'Resistant to DabTramtoCoCl2'
filtered_meta[which(all_data$OG_condition == "dabtramtocis" & all_data$Lineage %in% combined_lins_list$DabTramtoCis)] <- 'Resistant to DabTramtoCis'
filtered_meta[which(all_data$OG_condition == "cocl2" & all_data$Lineage %in% combined_lins_list$CoCl2)] <- 'Resistant to CoCl2'
filtered_meta[which(all_data$OG_condition == "cocl2todabtram" & all_data$Lineage %in% combined_lins_list$CoCl2toDabTram)] <- 'Resistant to CoCl2toDabTram'
filtered_meta[which(all_data$OG_condition == "cocl2tococl2" & all_data$Lineage %in% combined_lins_list$CoCl2toCoCl2)] <- 'Resistant to CoCl2toCoCl2'
filtered_meta[which(all_data$OG_condition == "cocl2tocis" & all_data$Lineage %in% combined_lins_list$CoCl2toCis)] <- 'Resistant to CoCl2toCis'
filtered_meta[which(all_data$OG_condition == "cis" & all_data$Lineage %in% combined_lins_list$Cis)] <- 'Resistant to Cis'
filtered_meta[which(all_data$OG_condition == "cistodabtram" & all_data$Lineage %in% combined_lins_list$CistoDabTram)] <- 'Resistant to CistoDabTram'
filtered_meta[which(all_data$OG_condition == "cistococl2" & all_data$Lineage %in% combined_lins_list$CistoCoCl2)] <- 'Resistant to CistoCoCl2'
filtered_meta[which(all_data$OG_condition == "cistocis" & all_data$Lineage %in% combined_lins_list$CistoCis)] <- 'Resistant to CistoCis'
#specify which cells are in lineages of more than 5 cells
filtered_meta[which(all_data$OG_condition == "dabtram" & all_data$Lineage %in% fivecell_cDNA$DabTram)] <- 'Large Resistant to DabTram'
filtered_meta[which(all_data$OG_condition == "dabtramtodabtram" & all_data$Lineage %in% fivecell_cDNA$DabTramtoDabTram)] <- 'Large Resistant to DabTramtoDabTram'
filtered_meta[which(all_data$OG_condition == "dabtramtococl2" & all_data$Lineage %in% fivecell_cDNA$DabTramtoCoCl2)] <- 'Large Resistant to DabTramtoCoCl2'
filtered_meta[which(all_data$OG_condition == "dabtramtocis" & all_data$Lineage %in% fivecell_cDNA$DabTramtoCis)] <- 'Large Resistant to DabTramtoCis'
filtered_meta[which(all_data$OG_condition == "cocl2" & all_data$Lineage %in% fivecell_cDNA$CoCl2)] <- 'Large Resistant to CoCl2'
filtered_meta[which(all_data$OG_condition == "cocl2todabtram" & all_data$Lineage %in% fivecell_cDNA$CoCl2toDabTram)] <- 'Large Resistant to CoCl2toDabTram'
filtered_meta[which(all_data$OG_condition == "cocl2tococl2" & all_data$Lineage %in% fivecell_cDNA$CoCl2toCoCl2)] <- 'Large Resistant to CoCl2toCoCl2'
filtered_meta[which(all_data$OG_condition == "cocl2tocis" & all_data$Lineage %in% fivecell_cDNA$CoCl2toCis)] <- 'Large Resistant to CoCl2toCis'
filtered_meta[which(all_data$OG_condition == "cis" & all_data$Lineage %in% fivecell_cDNA$Cis)] <- 'Large Resistant to Cis'
filtered_meta[which(all_data$OG_condition == "cistodabtram" & all_data$Lineage %in% fivecell_cDNA$CistoDabTram)] <- 'Large Resistant to CistoDabTram'
filtered_meta[which(all_data$OG_condition == "cistococl2" & all_data$Lineage %in% fivecell_cDNA$CistoCoCl2)] <- 'Large Resistant to CistoCoCl2'
filtered_meta[which(all_data$OG_condition == "cistocis" & all_data$Lineage %in% fivecell_cDNA$CistoCis)] <- 'Large Resistant to CistoCis'
# filtered_meta[which(all_data$OG_condition == "dabtram" & all_data$Lineage %in% fivecell_cDNA$DabTramMaintained)] <- 'Maintained Resistant to DabTram'
# filtered_meta[which(all_data$OG_condition == "dabtramtodabtram" & all_data$Lineage %in% fivecell_cDNA$DabTramMaintained)] <- 'Maintained Resistant to DabTramtoDabTram'
#specify which cells are in lineages that did not pass filtering
filtered_meta[which(all_data$OG_condition == "dabtram" & all_data$Lineage %nin% combined_lins_list$DabTram & all_data$Lineage %nin% c("No Barcode", "Still multiple"))] <- 'Filtered out'
filtered_meta[which(all_data$OG_condition == "dabtramtodabtram" & all_data$Lineage %nin% combined_lins_list$DabTramtoDabTram & all_data$Lineage %nin% c("No Barcode", "Still multiple"))] <- 'Filtered out'
filtered_meta[which(all_data$OG_condition == "dabtramtococl2" & all_data$Lineage %nin% combined_lins_list$DabTramtoCoCl2 & all_data$Lineage %nin% c("No Barcode", "Still multiple"))] <- 'Filtered out'
filtered_meta[which(all_data$OG_condition == "dabtramtocis" & all_data$Lineage %nin% combined_lins_list$DabTramtoCis & all_data$Lineage %nin% c("No Barcode", "Still multiple"))] <- 'Filtered out'
filtered_meta[which(all_data$OG_condition == "cocl2" & all_data$Lineage %nin% combined_lins_list$CoCl2 & all_data$Lineage %nin% c("No Barcode", "Still multiple"))] <- 'Filtered out'
filtered_meta[which(all_data$OG_condition == "cocl2todabtram" & all_data$Lineage %nin% combined_lins_list$CoCl2toDabTram & all_data$Lineage %nin% c("No Barcode", "Still multiple"))] <- 'Filtered out'
filtered_meta[which(all_data$OG_condition == "cocl2tococl2" & all_data$Lineage %nin% combined_lins_list$CoCl2toCoCl2 & all_data$Lineage %nin% c("No Barcode", "Still multiple"))] <- 'Filtered out'
filtered_meta[which(all_data$OG_condition == "cocl2tocis" & all_data$Lineage %nin% combined_lins_list$CoCl2toCis & all_data$Lineage %nin% c("No Barcode", "Still multiple"))] <- 'Filtered out'
filtered_meta[which(all_data$OG_condition == "cis" & all_data$Lineage %nin% combined_lins_list$Cis & all_data$Lineage %nin% c("No Barcode", "Still multiple"))] <- 'Filtered out'
filtered_meta[which(all_data$OG_condition == "cistodabtram" & all_data$Lineage %nin% combined_lins_list$CistoDabTram & all_data$Lineage %nin% c("No Barcode", "Still multiple"))] <- 'Filtered out'
filtered_meta[which(all_data$OG_condition == "cistococl2" & all_data$Lineage %nin% combined_lins_list$CistoCoCl2 & all_data$Lineage %nin% c("No Barcode", "Still multiple"))] <- 'Filtered out'
filtered_meta[which(all_data$OG_condition == "cistocis" & all_data$Lineage %nin% combined_lins_list$CistoCis & all_data$Lineage %nin% c("No Barcode", "Still multiple"))] <- 'Filtered out'
#specify which cells had zero or multiple barcodes
filtered_meta[which(all_data$Lineage %in% c("No Barcode", "Still multiple"))] <- 'No Barcode'
print(table(filtered_meta))filtered_meta
Filtered out Large Resistant to Cis Large Resistant to CistoCis Large Resistant to CistoCoCl2
3337 1375 951 2078
Large Resistant to CistoDabTram Large Resistant to CoCl2 Large Resistant to CoCl2toCis Large Resistant to CoCl2toCoCl2
1394 1784 3010 11578
Large Resistant to CoCl2toDabTram Large Resistant to DabTram Large Resistant to DabTramtoCis Large Resistant to DabTramtoCoCl2
663 478 4234 2840
Large Resistant to DabTramtoDabTram No Barcode Resistant to Cis Resistant to CistoCis
3176 35314 331 67
Resistant to CistoCoCl2 Resistant to CistoDabTram Resistant to CoCl2 Resistant to CoCl2toCis
135 113 278 157
Resistant to CoCl2toCoCl2 Resistant to CoCl2toDabTram Resistant to DabTram Resistant to DabTramtoCis
55 93 337 225
Resistant to DabTramtoCoCl2 Resistant to DabTramtoDabTram
100 222 all_data$Resistant_filtered <- filtered_meta
Idents(all_data) <- all_data$Resistant_filteredpdf('2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/test_plot.pdf', height = 10, width = 20)
DimPlot(all_data, group.by = 'ident', cols = )
DimPlot(all_data, group.by = "Lineage") + theme(legend.position = 'none')dev.off()null device
1 #Looking into the one lineage that switches ngfr -> egfr
pdf('2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/test_violin.pdf', height = 10, width = 30)
VlnPlot(all_data, features = 'NGFR', idents = 'Maintained Resistant to DabTram', group.by = "Lineage") + theme(legend.position = 'none') + geom_boxplot(fill = 'white', width = 0.1)Warning in SingleExIPlot(type = type, data = data[, x, drop = FALSE], idents = idents, :
All cells have the same value of NGFR.
Warning in max(data[, feature][is.finite(x = data[, feature])]) :
no non-missing arguments to max; returning -Inf
Warning in min(data[, feature]) :
no non-missing arguments to min; returning InfVlnPlot(all_data, features = 'EGFR', idents = 'Maintained Resistant to DabTram', group.by = "Lineage") + theme(legend.position = 'none') + geom_boxplot(fill = 'white', width = 0.1)Warning in SingleExIPlot(type = type, data = data[, x, drop = FALSE], idents = idents, :
All cells have the same value of EGFR.
Warning in max(data[, feature][is.finite(x = data[, feature])]) :
no non-missing arguments to max; returning -Inf
Warning in min(data[, feature]) :
no non-missing arguments to min; returning InfVlnPlot(all_data, features = 'NGFR', idents = 'Maintained Resistant to DabTramtoDabTram', group.by = "Lineage") + theme(legend.position = 'none') + geom_boxplot(fill = 'white', width = 0.1)Warning in SingleExIPlot(type = type, data = data[, x, drop = FALSE], idents = idents, :
All cells have the same value of NGFR.
Warning in max(data[, feature][is.finite(x = data[, feature])]) :
no non-missing arguments to max; returning -Inf
Warning in min(data[, feature]) :
no non-missing arguments to min; returning InfVlnPlot(all_data, features = 'EGFR', idents = 'Maintained Resistant to DabTramtoDabTram', group.by = "Lineage") + theme(legend.position = 'none') + geom_boxplot(fill = 'white', width = 0.1)Warning in SingleExIPlot(type = type, data = data[, x, drop = FALSE], idents = idents, :
All cells have the same value of EGFR.
Warning in max(data[, feature][is.finite(x = data[, feature])]) :
no non-missing arguments to max; returning -Inf
Warning in min(data[, feature]) :
no non-missing arguments to min; returning InfVlnPlot(all_data, features = 'NGFR', idents = 'Large Resistant to DabTram', group.by = "Lineage") + theme(legend.position = 'none') + geom_boxplot(fill = 'white', width = 0.1)
VlnPlot(all_data, features = 'EGFR', idents = 'Large Resistant to DabTram', group.by = "Lineage") + theme(legend.position = 'none') + geom_boxplot(fill = 'white', width = 0.1)VlnPlot(all_data, features = 'NGFR', idents = 'Large Resistant to DabTramtoDabTram', group.by = "Lineage") + theme(legend.position = 'none') + geom_boxplot(fill = 'white', width = 0.1)
VlnPlot(all_data, features = 'EGFR', idents = 'Large Resistant to DabTramtoDabTram', group.by = "Lineage") + theme(legend.position = 'none') + geom_boxplot(fill = 'white', width = 0.1)
dev.off()null device
1 #VlnPlot(all_data, features = 'NGFR', idents = all_data$OG_condition == 'dabtram', group.by = "lineage")#from dabtram_both_times, force 2 clusters in umap, plot vs ngfr egfr, find markers of these 2
switch_lin_dabtram <- names(all_data$orig.ident[all_data$Lineage == "Lin171516" & all_data$OG_condition == 'dabtram'])
switch_lin_dabtramtodabtram <- names(all_data$orig.ident[all_data$Lineage == "Lin171516" & all_data$OG_condition == 'dabtramtodabtram'])
DimPlot(all_data, group.by = "OG_condition", cols = c('dabtram' = '#623594', 'cocl2' = '#0F8241', 'cis' = '#C96D29', 'dabtramtodabtram' = '#561E59', 'dabtramtococl2' = '#A2248E', 'dabtramtocis' = '#9D85BE', 'cocl2todabtram' = '#10413B', 'cocl2tococl2' = '#6ABD45', 'cocl2tocis' = '#6DC49C', 'cistodabtram' = '#A23622', 'cistococl2' = '#F49129', 'cistocis' = '#FBD08C'))
DimPlot(all_data, cells.highlight = list(switch_lin_dabtram), cols.highlight = c('red'))
DimPlot(all_data, cells.highlight = list(switch_lin_dabtram, switch_lin_dabtramtodabtram), cols.highlight = c('blue', 'red'))
switch_lin <- names(dabtram$orig.ident[dabtram$Lineage == "Lin171516"])
DimPlot(dabtram)
DimPlot(dabtram, cells.highlight = list(switch_lin))
#Assign cluster assignments per lineage, find average score per lineage - make plots in order
dabtram_both_times_markers <- FindAllMarkers(dabtram_both_times, only.pos = TRUE, min.pct = 0.25, logfc.threshold = 0.25)Calculating cluster 0
| | 0 % ~calculating
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| | 0 % ~calculating
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DimPlot(dabtram_both_times, group.by = 'OG_condition')
FeaturePlot(dabtram_both_times, features = c('NGFR', 'EGFR', 'nFeature_RNA'))
Plot the number of cells per lineage in second drug for lineages that survived in DabTram
#average cell assignments per lineage in dabtram_maintained
#want to do a stacked barplot here-- copy paste code from condition clustering
#get lineage and cluster data from seurat object, switch cluster identifiers from 0,1 to -1,1 (egfr, ngfr)
clusters_per_lin <- data.frame (Cluster = as.numeric(as.character(dabtram_both_times$seurat_clusters)), Lineage = dabtram_both_times$Lineage, condition = dabtram_both_times$OG_condition)
clusters_per_lin$Cluster[clusters_per_lin$Cluster == 0] <- -1
clusters_per_lin_list <- list()
# Need to get percent values of EGFR for the lineage after the first treatment
for (i in fivecell_cDNA$DabTram){
currentlin <- filter(clusters_per_lin, clusters_per_lin$Lineage == i & clusters_per_lin$condition == 'dabtram')
Var1 <- c(-1,1)
Freq <- c(sum(filter(clusters_per_lin, clusters_per_lin$Lineage == i & clusters_per_lin$condition == 'dabtram')$Cluster == -1), # EGFR
sum(filter(clusters_per_lin, clusters_per_lin$Lineage == i & clusters_per_lin$condition == 'dabtram')$Cluster == 1))
clusters_per_lin_list[[i]] <- data.frame('Var1' = Var1, 'Freq' = Freq)
clusters_per_lin_list[[i]]$Score <- weighted.mean(as.numeric(as.character(clusters_per_lin_list[[i]]$Var1)), clusters_per_lin_list[[i]]$Freq)
}
# Build a dataframe for plotting based on % EGFR or NGFR per lineage after first treatment
clusters_per_lin_df <- data.frame(matrix(ncol = 5, nrow = 0))
colnames(clusters_per_lin_df) <- c('Lineage', 'Percent_cells', 'Num_cells', 'Score')
for (i in names(clusters_per_lin_list)){
clusters_per_lin_df <- rbind(clusters_per_lin_df, data.frame('Lineage' = i, 'Percent_cells' = clusters_per_lin_list[[i]]$Freq[clusters_per_lin_list[[i]]$Var1 == 1]/sum(clusters_per_lin_list[[i]]$Freq), 'Num_cells' = sum(clusters_per_lin_list[[i]]$Freq[clusters_per_lin_list[[i]]$Var1 == 1]), 'Score' = clusters_per_lin_list[[i]]$Score[1], 'Cluster' = 'NGFR')) # Add NGFR row
clusters_per_lin_df <- rbind(clusters_per_lin_df, data.frame('Lineage' = i, 'Percent_cells' = clusters_per_lin_list[[i]]$Freq[clusters_per_lin_list[[i]]$Var1 == -1]/sum(clusters_per_lin_list[[i]]$Freq), 'Num_cells' = sum(clusters_per_lin_list[[i]]$Freq[clusters_per_lin_list[[i]]$Var1 == -1]), 'Score' = clusters_per_lin_list[[i]]$Score[1], 'Cluster' = 'EGFR')) # Add EGFR row
}
# Reorder so that EGFR dominant lineages are plot first
clusters_per_lin_df <- clusters_per_lin_df[with(clusters_per_lin_df, order(-Score,Num_cells )),]
clusters_per_lin_df$Lineage <- factor(clusters_per_lin_df$Lineage, levels = rev(unique(clusters_per_lin_df$Lineage)))
# make list object for after second treatment
clusters_per_lin_list_sec <- list()
# Need to get percent values of EGFR for the lineage after the second treatment
for (i in fivecell_cDNA$DabTram){
currentlin <- filter(clusters_per_lin, clusters_per_lin$Lineage == i & clusters_per_lin$condition == 'dabtramtodabtram')
Var1 <- c(-1,1)
Freq <- c(sum(filter(clusters_per_lin, clusters_per_lin$Lineage == i & clusters_per_lin$condition == 'dabtramtodabtram')$Cluster == -1), # EGFR
sum(filter(clusters_per_lin, clusters_per_lin$Lineage == i & clusters_per_lin$condition == 'dabtramtodabtram')$Cluster == 1))
clusters_per_lin_list_sec[[i]] <- data.frame('Var1' = Var1, 'Freq' = Freq)
clusters_per_lin_list_sec[[i]]$Score <- weighted.mean(as.numeric(as.character(clusters_per_lin_list_sec[[i]]$Var1)), clusters_per_lin_list_sec[[i]]$Freq)
}
# Build a dataframe for plotting based on % EGFR or NGFR per lineage after second treatment
clusters_per_lin_df_sec <- data.frame(matrix(ncol = 5, nrow = 0))
colnames(clusters_per_lin_df_sec) <- c('Lineage', 'Percent_cells', 'Num_cells', 'Score', 'Cluster')
for (i in names(clusters_per_lin_list)){
if (sum(clusters_per_lin_list_sec[[i]]$Freq) < 5){
clusters_per_lin_df_sec <- rbind(clusters_per_lin_df_sec, data.frame('Lineage' = i, 'Percent_cells' = 1, 'Num_cells' = 0, 'Score' = 0, 'Cluster' = 'Died')) # Add Lineage died row
clusters_per_lin_df_sec <- rbind(clusters_per_lin_df_sec, data.frame('Lineage' = i, 'Percent_cells' = 0, 'Num_cells' = 0, 'Score' = 0, 'Cluster' = 'NGFR')) # Add NGFR row
clusters_per_lin_df_sec <- rbind(clusters_per_lin_df_sec, data.frame('Lineage' = i, 'Percent_cells' = 0, 'Num_cells' = 0, 'Score' = 0, 'Cluster' = 'EGFR')) # Add EGFR row
}else{ # Actually calculate percentages since the lineage survived/is more than 5 cells
clusters_per_lin_df_sec <- rbind(clusters_per_lin_df_sec, data.frame('Lineage' = i, 'Percent_cells' = 0, 'Num_cells' = 0, 'Score' = clusters_per_lin_list_sec[[i]]$Score[1], 'Cluster' = 'Died')) # Add Lineage died row
clusters_per_lin_df_sec <- rbind(clusters_per_lin_df_sec, data.frame('Lineage' = i, 'Percent_cells' = clusters_per_lin_list_sec[[i]]$Freq[clusters_per_lin_list_sec[[i]]$Var1 == 1]/sum(clusters_per_lin_list_sec[[i]]$Freq), 'Num_cells' = sum(clusters_per_lin_list_sec[[i]]$Freq[clusters_per_lin_list_sec[[i]]$Var1 == 1]), 'Score' = clusters_per_lin_list_sec[[i]]$Score[1], 'Cluster' = 'NGFR')) # Add NGFR row
clusters_per_lin_df_sec <- rbind(clusters_per_lin_df_sec, data.frame('Lineage' = i, 'Percent_cells' = clusters_per_lin_list_sec[[i]]$Freq[clusters_per_lin_list_sec[[i]]$Var1 == -1]/sum(clusters_per_lin_list_sec[[i]]$Freq), 'Num_cells' = sum(clusters_per_lin_list_sec[[i]]$Freq[clusters_per_lin_list_sec[[i]]$Var1 == -1]), 'Score' = clusters_per_lin_list_sec[[i]]$Score[1], 'Cluster' = 'EGFR')) # Add EGFR row
}
}
# Reorder so that EGFR dominant lineages are plot first
clusters_per_lin_df_sec$Lineage <- factor(clusters_per_lin_df_sec$Lineage, levels = rev(unique(clusters_per_lin_df$Lineage)))
# Plot
p1 <- ggplot(clusters_per_lin_df, aes(y = Percent_cells, x = Lineage, fill = Cluster)) + geom_bar(stat = 'identity', position = position_fill(reverse = T), color = '#D3D3D3') + scale_fill_manual(values = c("#000000","#FFFFFF")) + theme_classic() + theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))
p2 <- ggplot(clusters_per_lin_df_sec, aes(y = Percent_cells, x = Lineage, fill = Cluster)) + geom_bar(stat = 'identity', position = position_fill(reverse = T), color = '#D3D3D3') + scale_fill_manual(values = c("#FF0000","#000000", '#FFFFFF')) + theme_classic() + theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))
pdf('2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/stacked_bar_EGFR_NGFR_Died.pdf')
ggarrange(p1,p2, nrow = 2)
dev.off()null device
1 Look at whether lineages cluster together in each individual condition - Starting with DabTram
# Get the number of cells in the DabTram resistant lineages in the conditions that got DabTram first
dabtramtococl2_dt_resist_lins <- list()
dabtramtocis_dt_resist_lins <- list()
for (i in fivecell_cDNA$DabTram){
dabtramtococl2_dt_resist_lins[[i]] <- all_data$Lineage[all_data$Lineage == i & all_data$OG_condition == 'dabtramtococl2']
dabtramtocis_dt_resist_lins[[i]] <- all_data$Lineage[all_data$Lineage == i & all_data$OG_condition == 'dabtramtocis']
}
# convert list to dataframe
dabtramtococl2_dt_resist_lins_df <- data.frame(num_cells = lengths(dabtramtococl2_dt_resist_lins))
dabtramtococl2_dt_resist_lins_df$Lineage = rownames(dabtramtococl2_dt_resist_lins_df)
dabtramtococl2_dt_resist_lins_df$Lineage <- factor(dabtramtococl2_dt_resist_lins_df$Lineage, levels = rev(unique(clusters_per_lin_df$Lineage)))
dabtramtocis_dt_resist_lins_df <- data.frame(num_cells = lengths(dabtramtocis_dt_resist_lins))
dabtramtocis_dt_resist_lins_df$Lineage = rownames(dabtramtocis_dt_resist_lins_df)
dabtramtocis_dt_resist_lins_df$Lineage <- factor(dabtramtocis_dt_resist_lins_df$Lineage, levels = rev(unique(clusters_per_lin_df$Lineage)))
# Plot
p3 <- ggplot(dabtramtococl2_dt_resist_lins_df, aes(y = log(num_cells+1), x = Lineage)) + geom_col(fill = '#A2248E') + theme_classic() + theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1)) + labs(title = 'DabTram to CoCl2')
p4 <- ggplot(dabtramtocis_dt_resist_lins_df, aes(y = log(num_cells+1), x = Lineage)) + geom_col(fill = '#9D85BE') + theme_classic() + theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1)) + labs(title = 'DabTram to Cis')
pdf('2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/stacked_bar_EGFR_NGFR_Died_w_other_second_drugs.pdf', height = 14)
ggarrange(p1+ theme(legend.position = 'bottom'),p2+ theme(legend.position = 'bottom'),p3,p4, nrow = 4)
dev.off()null device
1 Significance testing of the dabtram simulation
# Find the mean of the maximum percentage of cells in a single cluster per lineage in each simulation
mean_dabtram <- mean(unlist(lapply(dabtram_lin_clust_list, function(x) max(x$Pcnt_cells))))
means_dabtram_sim <- sapply(1:length(dabtram_lin_clust_rand_list), function(y)
mean(unlist(lapply(dabtram_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells)))))
z_mean_dabtram <- (mean_dabtram-mean(means_dabtram_sim))/sd(means_dabtram_sim)
pval_mean_dabtram <- pnorm(z_mean_dabtram, mean(means_dabtram_sim), sd(means_dabtram_sim), lower.tail = F)
# Find the weighted mean of the maximum percentage of cells in a single cluster per lineage in each simulation
weighted_mean_dabtram <- weighted.mean(unlist(lapply(dabtram_lin_clust_list, function(x) max(x$Pcnt_cells))),
unlist(lapply(dabtram_lin_clust_list, function(x) sum(x$Num_cells))))
weighted_means_dabtram_sim <- sapply(1:length(dabtram_lin_clust_rand_list), function(y)
weighted.mean(unlist(lapply(dabtram_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells))),
unlist(lapply(dabtram_lin_clust_rand_list[[y]], function(x) sum(x$Num_cells)))))
z_wmean_dabtram <- (weighted_mean_dabtram-mean(weighted_means_dabtram_sim))/sd(weighted_means_dabtram_sim)
pval_wmean_dabtram <- pnorm(z_wmean_dabtram, mean(weighted_means_dabtram_sim), sd(weighted_means_dabtram_sim), lower.tail = F)
# Compare each individual distribution of maxes to the observed max by t test and track pval
ttest_pval_dabtram <- c()
for (i in 1:length(means_dabtram_sim)){
sim_maxes <- unlist(lapply(dabtram_lin_clust_rand_list[[i]], function(x) max(x$Pcnt_cells)))
ttest_pval_dabtram <- cbind(ttest_pval_dabtram,t.test(x = unlist(lapply(dabtram_lin_clust_list, function(x) max(x$Pcnt_cells))),y = unlist(lapply(dabtram_lin_clust_rand_list[[i]], function(x) max(x$Pcnt_cells))), alternative = 'greater')$p.value)
}
# Save outputs
save(dabtram_lin_clust_list, dabtram_lin_clust_rand_list,z_mean_dabtram,pval_mean_dabtram, z_wmean_dabtram, ttest_pval_dabtram, file = '2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/dabtram_sim_results.RData')
rm(dabtram)Look at whether lineages cluster together in each individual condition - DabTramtoDabTram
Idents(all_data) <- all_data$OG_condition # Change the idents to the OG condition for subsetting to dabtramtodabtram
dabtramtodabtram <- subset(all_data, idents = 'dabtramtodabtram') # Subset down to the dabtramtodabtram object
ElbowPlot(dabtramtodabtram) # The standard deviation seems to really level off at 10
# Recluster with the appropriate number of dimensions
dabtramtodabtram <- FindNeighbors(dabtramtodabtram, dims = 1:10)
dabtramtodabtram <- FindClusters(dabtramtodabtram, resolution = 0.5)
dabtramtodabtram <- RunUMAP(dabtramtodabtram, dims = 1:10)
DimPlot(dabtramtodabtram, reduction = 'umap')
# Build list of lineages with at least 5 cells in dab tram and check what seurat cluster the cells are in
dabtramtodabtram_lin_clust_list <- list()
for (i in fivecell_cDNA$DabTramtoDabTram){
temp_df <- as.data.frame(table(Idents(dabtramtodabtram)[dabtramtodabtram$Lineage == i]))
colnames(temp_df) <- c('Seurat_clust', 'Num_cells')
temp_df$Pcnt_cells <- temp_df$Num_cells/sum(temp_df$Num_cells)
dabtramtodabtram_lin_clust_list[[i]] <- temp_df
}
# Need to do a random sampling of the same thing
dabtramtodabtram_lin_clust_rand_list <- list()
num_iter <- 800
for(j in 1:num_iter){
set.seed(j)
dabtramtodabtram_lin_clust_rand_list[[j]] <- list()
for (i in fivecell_cDNA$DabTramtoDabTram){
num_cells <- length(dabtramtodabtram$Lineage[dabtramtodabtram$Lineage == i])
temp_df <- as.data.frame(table(sample(Idents(dabtramtodabtram), num_cells, replace = F)))
colnames(temp_df) <- c('Seurat_clust', 'Num_cells')
temp_df$Pcnt_cells <- temp_df$Num_cells/sum(temp_df$Num_cells)
dabtramtodabtram_lin_clust_rand_list[[j]][[i]] <- temp_df
}
}Significance testing of the dabtramtodabtram simulation
# Find the mean of the maximum percentage of cells in a single cluster per lineage in each simulation
mean_dabtramtodabtram <- mean(unlist(lapply(dabtramtodabtram_lin_clust_list, function(x) max(x$Pcnt_cells))))
means_dabtramtodabtram_sim <- sapply(1:length(dabtramtodabtram_lin_clust_rand_list), function(y)
mean(unlist(lapply(dabtramtodabtram_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells)))))
z_mean_dabtramtodabtram <- (mean_dabtramtodabtram-mean(means_dabtramtodabtram_sim))/sd(means_dabtramtodabtram_sim)
pval_mean_dabtramtodabtram <- pnorm(z_mean_dabtramtodabtram, mean(means_dabtramtodabtram_sim), sd(means_dabtramtodabtram_sim), lower.tail = F)
# Find the weighted mean of the maximum percentage of cells in a single cluster per lineage in each simulation
weighted_mean_dabtramtodabtram <- weighted.mean(unlist(lapply(dabtramtodabtram_lin_clust_list, function(x) max(x$Pcnt_cells))),
unlist(lapply(dabtramtodabtram_lin_clust_list, function(x) sum(x$Num_cells))))
weighted_means_dabtramtodabtram_sim <- sapply(1:length(dabtramtodabtram_lin_clust_rand_list), function(y)
weighted.mean(unlist(lapply(dabtramtodabtram_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells))),
unlist(lapply(dabtramtodabtram_lin_clust_rand_list[[y]], function(x) sum(x$Num_cells)))))
z_wmean_dabtramtodabtram <- (weighted_mean_dabtramtodabtram-mean(weighted_means_dabtramtodabtram_sim))/sd(weighted_means_dabtramtodabtram_sim)
pval_wmean_dabtramtodabtram <- pnorm(z_wmean_dabtramtodabtram, mean(weighted_means_dabtramtodabtram_sim), sd(weighted_means_dabtramtodabtram_sim), lower.tail = F)
# Compare each individual distribution of maxes to the observed max by t test and track pval
ttest_pval_dabtramtodabtram <- c()
for (i in 1:length(means_dabtramtodabtram_sim)){
sim_maxes <- unlist(lapply(dabtramtodabtram_lin_clust_rand_list[[i]], function(x) max(x$Pcnt_cells)))
ttest_pval_dabtramtodabtram <- cbind(ttest_pval_dabtramtodabtram,t.test(x = unlist(lapply(dabtramtodabtram_lin_clust_list, function(x) max(x$Pcnt_cells))),y = unlist(lapply(dabtramtodabtram_lin_clust_rand_list[[i]], function(x) max(x$Pcnt_cells))), alternative = 'greater')$p.value)
}
# Save outputs
save(dabtramtodabtram_lin_clust_list, dabtramtodabtram_lin_clust_rand_list,z_mean_dabtramtodabtram,pval_mean_dabtramtodabtram, z_wmean_dabtramtodabtram, ttest_pval_dabtramtodabtram, file = '2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/dabtramtodabtram_sim_results.RData')
rm(dabtramtodabtram)Look at whether lineages cluster together in each individual condition - DabTramtoCis
Idents(all_data) <- all_data$OG_condition # Change the idents to the OG condition for subsetting to dabtramtocis
dabtramtocis <- subset(all_data, idents = 'dabtramtocis') # Subset down to the dabtramtocis object
ElbowPlot(dabtramtocis) # The standard deviation seems to really level off at 10
# Recluster with the appropriate number of dimensions
dabtramtocis <- FindNeighbors(dabtramtocis, dims = 1:10)
dabtramtocis <- FindClusters(dabtramtocis, resolution = 0.5)
dabtramtocis <- RunUMAP(dabtramtocis, dims = 1:10)
DimPlot(dabtramtocis, reduction = 'umap')
# Build list of lineages with at least 5 cells in dab tram and check what seurat cluster the cells are in
dabtramtocis_lin_clust_list <- list()
for (i in fivecell_cDNA$DabTramtoCis){
temp_df <- as.data.frame(table(Idents(dabtramtocis)[dabtramtocis$Lineage == i]))
colnames(temp_df) <- c('Seurat_clust', 'Num_cells')
temp_df$Pcnt_cells <- temp_df$Num_cells/sum(temp_df$Num_cells)
dabtramtocis_lin_clust_list[[i]] <- temp_df
}
# Need to do a random sampling of the same thing
dabtramtocis_lin_clust_rand_list <- list()
num_iter <- 800
for(j in 1:num_iter){
set.seed(j)
dabtramtocis_lin_clust_rand_list[[j]] <- list()
for (i in fivecell_cDNA$DabTramtoCis){
num_cells <- length(dabtramtocis$Lineage[dabtramtocis$Lineage == i])
temp_df <- as.data.frame(table(sample(Idents(dabtramtocis), num_cells, replace = F)))
colnames(temp_df) <- c('Seurat_clust', 'Num_cells')
temp_df$Pcnt_cells <- temp_df$Num_cells/sum(temp_df$Num_cells)
dabtramtocis_lin_clust_rand_list[[j]][[i]] <- temp_df
}
}Significance testing of the dabtramtocis simulation
# Find the mean of the maximum percentage of cells in a single cluster per lineage in each simulation
mean_dabtramtocis <- mean(unlist(lapply(dabtramtocis_lin_clust_list, function(x) max(x$Pcnt_cells))))
means_dabtramtocis_sim <- sapply(1:length(dabtramtocis_lin_clust_rand_list), function(y)
mean(unlist(lapply(dabtramtocis_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells)))))
z_mean_dabtramtocis <- (mean_dabtramtocis-mean(means_dabtramtocis_sim))/sd(means_dabtramtocis_sim)
pval_mean_dabtramtocis <- pnorm(z_mean_dabtramtocis, mean(means_dabtramtocis_sim), sd(means_dabtramtocis_sim), lower.tail = F)
# Find the weighted mean of the maximum percentage of cells in a single cluster per lineage in each simulation
weighted_mean_dabtramtocis <- weighted.mean(unlist(lapply(dabtramtocis_lin_clust_list, function(x) max(x$Pcnt_cells))),
unlist(lapply(dabtramtocis_lin_clust_list, function(x) sum(x$Num_cells))))
weighted_means_dabtramtocis_sim <- sapply(1:length(dabtramtocis_lin_clust_rand_list), function(y)
weighted.mean(unlist(lapply(dabtramtocis_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells))),
unlist(lapply(dabtramtocis_lin_clust_rand_list[[y]], function(x) sum(x$Num_cells)))))
z_wmean_dabtramtocis <- (weighted_mean_dabtramtocis-mean(weighted_means_dabtramtocis_sim))/sd(weighted_means_dabtramtocis_sim)
pval_wmean_dabtramtocis <- pnorm(z_wmean_dabtramtocis, mean(weighted_means_dabtramtocis_sim), sd(weighted_means_dabtramtocis_sim), lower.tail = F)
# Compare each individual distribution of maxes to the observed max by t test and track pval
ttest_pval_dabtramtocis <- c()
for (i in 1:length(means_dabtramtocis_sim)){
sim_maxes <- unlist(lapply(dabtramtocis_lin_clust_rand_list[[i]], function(x) max(x$Pcnt_cells)))
ttest_pval_dabtramtocis <- cbind(ttest_pval_dabtramtocis,t.test(x = unlist(lapply(dabtramtocis_lin_clust_list, function(x) max(x$Pcnt_cells))),y = unlist(lapply(dabtramtocis_lin_clust_rand_list[[i]], function(x) max(x$Pcnt_cells))), alternative = 'greater')$p.value)
}
# Save outputs
save(dabtramtocis_lin_clust_list, dabtramtocis_lin_clust_rand_list,z_mean_dabtramtocis,pval_mean_dabtramtocis, z_wmean_dabtramtocis, ttest_pval_dabtramtocis, file = '2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/dabtramtocis_sim_results.RData')
rm(dabtramtocis)Look at whether lineages cluster together in each individual condition - DabTramtoCis
Idents(all_data) <- all_data$OG_condition # Change the idents to the OG condition for subsetting to dabtramtococl2
dabtramtococl2 <- subset(all_data, idents = 'dabtramtococl2') # Subset down to the dabtramtococl2 object
ElbowPlot(dabtramtococl2) # The standard deviation seems to really level off at 10
# Recluster with the appropriate number of dimensions
dabtramtococl2 <- FindNeighbors(dabtramtococl2, dims = 1:10)
dabtramtococl2 <- FindClusters(dabtramtococl2, resolution = 0.5)
dabtramtococl2 <- RunUMAP(dabtramtococl2, dims = 1:10)
DimPlot(dabtramtococl2, reduction = 'umap')
# Build list of lineages with at least 5 cells in dab tram and check what seurat cluster the cells are in
dabtramtococl2_lin_clust_list <- list()
for (i in fivecell_cDNA$DabTramtoCoCl2){
temp_df <- as.data.frame(table(Idents(dabtramtococl2)[dabtramtococl2$Lineage == i]))
colnames(temp_df) <- c('Seurat_clust', 'Num_cells')
temp_df$Pcnt_cells <- temp_df$Num_cells/sum(temp_df$Num_cells)
dabtramtococl2_lin_clust_list[[i]] <- temp_df
}
# Need to do a random sampling of the same thing
dabtramtococl2_lin_clust_rand_list <- list()
num_iter <- 800
for(j in 1:num_iter){
set.seed(j)
dabtramtococl2_lin_clust_rand_list[[j]] <- list()
for (i in fivecell_cDNA$DabTramtoCoCl2){
num_cells <- length(dabtramtococl2$Lineage[dabtramtococl2$Lineage == i])
temp_df <- as.data.frame(table(sample(Idents(dabtramtococl2), num_cells, replace = F)))
colnames(temp_df) <- c('Seurat_clust', 'Num_cells')
temp_df$Pcnt_cells <- temp_df$Num_cells/sum(temp_df$Num_cells)
dabtramtococl2_lin_clust_rand_list[[j]][[i]] <- temp_df
}
}Significance testing of the dabtramtococl2 simulation
# Find the mean of the maximum percentage of cells in a single cluster per lineage in each simulation
mean_dabtramtococl2 <- mean(unlist(lapply(dabtramtococl2_lin_clust_list, function(x) max(x$Pcnt_cells))))
means_dabtramtococl2_sim <- sapply(1:length(dabtramtococl2_lin_clust_rand_list), function(y)
mean(unlist(lapply(dabtramtococl2_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells)))))
z_mean_dabtramtococl2 <- (mean_dabtramtococl2-mean(means_dabtramtococl2_sim))/sd(means_dabtramtococl2_sim)
pval_mean_dabtramtococl2 <- pnorm(z_mean_dabtramtococl2, mean(means_dabtramtococl2_sim), sd(means_dabtramtococl2_sim), lower.tail = F)
# Find the weighted mean of the maximum percentage of cells in a single cluster per lineage in each simulation
weighted_mean_dabtramtococl2 <- weighted.mean(unlist(lapply(dabtramtococl2_lin_clust_list, function(x) max(x$Pcnt_cells))),
unlist(lapply(dabtramtococl2_lin_clust_list, function(x) sum(x$Num_cells))))
weighted_means_dabtramtococl2_sim <- sapply(1:length(dabtramtococl2_lin_clust_rand_list), function(y)
weighted.mean(unlist(lapply(dabtramtococl2_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells))),
unlist(lapply(dabtramtococl2_lin_clust_rand_list[[y]], function(x) sum(x$Num_cells)))))
z_wmean_dabtramtococl2 <- (weighted_mean_dabtramtococl2-mean(weighted_means_dabtramtococl2_sim))/sd(weighted_means_dabtramtococl2_sim)
pval_wmean_dabtramtococl2 <- pnorm(z_wmean_dabtramtococl2, mean(weighted_means_dabtramtococl2_sim), sd(weighted_means_dabtramtococl2_sim), lower.tail = F)
# Compare each individual distribution of maxes to the observed max by t test and track pval
ttest_pval_dabtramtococl2 <- c()
for (i in 1:length(means_dabtramtococl2_sim)){
sim_maxes <- unlist(lapply(dabtramtococl2_lin_clust_rand_list[[i]], function(x) max(x$Pcnt_cells)))
ttest_pval_dabtramtococl2 <- cbind(ttest_pval_dabtramtococl2,t.test(x = unlist(lapply(dabtramtococl2_lin_clust_list, function(x) max(x$Pcnt_cells))),y = unlist(lapply(dabtramtococl2_lin_clust_rand_list[[i]], function(x) max(x$Pcnt_cells))), alternative = 'greater')$p.value)
}
# Save outputs
save(dabtramtococl2_lin_clust_list, dabtramtococl2_lin_clust_rand_list,z_mean_dabtramtococl2,pval_mean_dabtramtococl2, z_wmean_dabtramtococl2, ttest_pval_dabtramtococl2, file = '2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/dabtramtococl2_sim_results.RData')
rm(dabtramtococl2)Look at whether lineages cluster together in each individual condition - Starting with DabTram
Idents(all_data) <- all_data$OG_condition # Change the idents to the OG condition for subsetting to cocl2
cocl2 <- subset(all_data, idents = 'cocl2') # Subset down to the cocl2 object
ElbowPlot(cocl2) # The standard deviation seems to really level off at 10
# Recluster with the appropriate number of dimensions
cocl2 <- FindNeighbors(cocl2, dims = 1:10)
cocl2 <- FindClusters(cocl2, resolution = 0.5)
cocl2 <- RunUMAP(cocl2, dims = 1:10)
DimPlot(cocl2, reduction = 'umap')
# Build list of lineages with at least 5 cells in dab tram and check what seurat cluster the cells are in
cocl2_lin_clust_list <- list()
for (i in fivecell_cDNA$CoCl2){
temp_df <- as.data.frame(table(Idents(cocl2)[cocl2$Lineage == i]))
colnames(temp_df) <- c('Seurat_clust', 'Num_cells')
temp_df$Pcnt_cells <- temp_df$Num_cells/sum(temp_df$Num_cells)
cocl2_lin_clust_list[[i]] <- temp_df
}
# Need to do a random sampling of the same thing
cocl2_lin_clust_rand_list <- list()
num_iter <- 800
for(j in 1:num_iter){
set.seed(j)
cocl2_lin_clust_rand_list[[j]] <- list()
for (i in fivecell_cDNA$CoCl2){
num_cells <- length(cocl2$Lineage[cocl2$Lineage == i])
temp_df <- as.data.frame(table(sample(Idents(cocl2), num_cells, replace = F)))
colnames(temp_df) <- c('Seurat_clust', 'Num_cells')
temp_df$Pcnt_cells <- temp_df$Num_cells/sum(temp_df$Num_cells)
cocl2_lin_clust_rand_list[[j]][[i]] <- temp_df
}
}Significance testing of the cocl2 simulation
# Find the mean of the maximum percentage of cells in a single cluster per lineage in each simulation
mean_cocl2 <- mean(unlist(lapply(cocl2_lin_clust_list, function(x) max(x$Pcnt_cells))))
means_cocl2_sim <- sapply(1:length(cocl2_lin_clust_rand_list), function(y)
mean(unlist(lapply(cocl2_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells)))))
z_mean_cocl2 <- (mean_cocl2-mean(means_cocl2_sim))/sd(means_cocl2_sim)
pval_mean_cocl2 <- pnorm(z_mean_cocl2, mean(means_cocl2_sim), sd(means_cocl2_sim), lower.tail = F)
# Find the weighted mean of the maximum percentage of cells in a single cluster per lineage in each simulation
weighted_mean_cocl2 <- weighted.mean(unlist(lapply(cocl2_lin_clust_list, function(x) max(x$Pcnt_cells))),
unlist(lapply(cocl2_lin_clust_list, function(x) sum(x$Num_cells))))
weighted_means_cocl2_sim <- sapply(1:length(cocl2_lin_clust_rand_list), function(y)
weighted.mean(unlist(lapply(cocl2_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells))),
unlist(lapply(cocl2_lin_clust_rand_list[[y]], function(x) sum(x$Num_cells)))))
z_wmean_cocl2 <- (weighted_mean_cocl2-mean(weighted_means_cocl2_sim))/sd(weighted_means_cocl2_sim)
pval_wmean_cocl2 <- pnorm(z_wmean_cocl2, mean(weighted_means_cocl2_sim), sd(weighted_means_cocl2_sim), lower.tail = F)
# Compare each individual distribution of maxes to the observed max by t test and track pval
ttest_pval_cocl2 <- c()
for (i in 1:length(means_cocl2_sim)){
sim_maxes <- unlist(lapply(cocl2_lin_clust_rand_list[[i]], function(x) max(x$Pcnt_cells)))
ttest_pval_cocl2 <- cbind(ttest_pval_cocl2,t.test(x = unlist(lapply(cocl2_lin_clust_list, function(x) max(x$Pcnt_cells))),y = unlist(lapply(cocl2_lin_clust_rand_list[[i]], function(x) max(x$Pcnt_cells))), alternative = 'greater')$p.value)
}
# Save outputs
save(cocl2_lin_clust_list, cocl2_lin_clust_rand_list,z_mean_cocl2,pval_mean_cocl2, z_wmean_cocl2, ttest_pval_cocl2, file = '2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/cocl2_sim_results.RData')
rm(cocl2)Look at whether lineages cluster together in each individual condition - Starting with CoCl2toCoCl2
Idents(all_data) <- all_data$OG_condition # Change the idents to the OG condition for subsetting to cocl2tococl2
cocl2tococl2 <- subset(all_data, idents = 'cocl2tococl2') # Subset down to the cocl2tococl2 object
ElbowPlot(cocl2tococl2) # The standard deviation seems to really level off at 10
# Recluster with the appropriate number of dimensions
cocl2tococl2 <- FindNeighbors(cocl2tococl2, dims = 1:10)
cocl2tococl2 <- FindClusters(cocl2tococl2, resolution = 0.5)
cocl2tococl2 <- RunUMAP(cocl2tococl2, dims = 1:10)
DimPlot(cocl2tococl2, reduction = 'umap')
# Build list of lineages with at least 5 cells in dab tram and check what seurat cluster the cells are in
cocl2tococl2_lin_clust_list <- list()
for (i in fivecell_cDNA$CoCl2toCoCl2){
temp_df <- as.data.frame(table(Idents(cocl2tococl2)[cocl2tococl2$Lineage == i]))
colnames(temp_df) <- c('Seurat_clust', 'Num_cells')
temp_df$Pcnt_cells <- temp_df$Num_cells/sum(temp_df$Num_cells)
cocl2tococl2_lin_clust_list[[i]] <- temp_df
}
# Need to do a random sampling of the same thing
cocl2tococl2_lin_clust_rand_list <- list()
num_iter <- 800
for(j in 1:num_iter){
set.seed(j)
cocl2tococl2_lin_clust_rand_list[[j]] <- list()
for (i in fivecell_cDNA$CoCl2toCoCl2){
num_cells <- length(cocl2tococl2$Lineage[cocl2tococl2$Lineage == i])
temp_df <- as.data.frame(table(sample(Idents(cocl2tococl2), num_cells, replace = F)))
colnames(temp_df) <- c('Seurat_clust', 'Num_cells')
temp_df$Pcnt_cells <- temp_df$Num_cells/sum(temp_df$Num_cells)
cocl2tococl2_lin_clust_rand_list[[j]][[i]] <- temp_df
}
}Significance testing of the cocl2tococl2 simulation
# Find the mean of the maximum percentage of cells in a single cluster per lineage in each simulation
mean_cocl2tococl2 <- mean(unlist(lapply(cocl2tococl2_lin_clust_list, function(x) max(x$Pcnt_cells))))
means_cocl2tococl2_sim <- sapply(1:length(cocl2tococl2_lin_clust_rand_list), function(y)
mean(unlist(lapply(cocl2tococl2_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells)))))
z_mean_cocl2tococl2 <- (mean_cocl2tococl2-mean(means_cocl2tococl2_sim))/sd(means_cocl2tococl2_sim)
pval_mean_cocl2tococl2 <- pnorm(z_mean_cocl2tococl2, mean(means_cocl2tococl2_sim), sd(means_cocl2tococl2_sim), lower.tail = F)
# Find the weighted mean of the maximum percentage of cells in a single cluster per lineage in each simulation
weighted_mean_cocl2tococl2 <- weighted.mean(unlist(lapply(cocl2tococl2_lin_clust_list, function(x) max(x$Pcnt_cells))),
unlist(lapply(cocl2tococl2_lin_clust_list, function(x) sum(x$Num_cells))))
weighted_means_cocl2tococl2_sim <- sapply(1:length(cocl2tococl2_lin_clust_rand_list), function(y)
weighted.mean(unlist(lapply(cocl2tococl2_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells))),
unlist(lapply(cocl2tococl2_lin_clust_rand_list[[y]], function(x) sum(x$Num_cells)))))
z_wmean_cocl2tococl2 <- (weighted_mean_cocl2tococl2-mean(weighted_means_cocl2tococl2_sim))/sd(weighted_means_cocl2tococl2_sim)
pval_wmean_cocl2tococl2 <- pnorm(z_wmean_cocl2tococl2, mean(weighted_means_cocl2tococl2_sim), sd(weighted_means_cocl2tococl2_sim), lower.tail = F)
# Compare each individual distribution of maxes to the observed max by t test and track pval
ttest_pval_cocl2tococl2 <- c()
for (i in 1:length(means_cocl2tococl2_sim)){
sim_maxes <- unlist(lapply(cocl2tococl2_lin_clust_rand_list[[i]], function(x) max(x$Pcnt_cells)))
ttest_pval_cocl2tococl2 <- cbind(ttest_pval_cocl2tococl2,t.test(x = unlist(lapply(cocl2tococl2_lin_clust_list, function(x) max(x$Pcnt_cells))),y = unlist(lapply(cocl2tococl2_lin_clust_rand_list[[i]], function(x) max(x$Pcnt_cells))), alternative = 'greater')$p.value)
}
# Save outputs
save(cocl2tococl2_lin_clust_list, cocl2tococl2_lin_clust_rand_list,z_mean_cocl2tococl2,pval_mean_cocl2tococl2, z_wmean_cocl2tococl2, ttest_pval_cocl2tococl2, file = '2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/cocl2tococl2_sim_results.RData')
rm(cocl2tococl2)Look at whether lineages cluster together in each individual condition - Starting with CoCl2toCis
Idents(all_data) <- all_data$OG_condition # Change the idents to the OG condition for subsetting to cocl2tocis
cocl2tocis <- subset(all_data, idents = 'cocl2tocis') # Subset down to the cocl2tocis object
ElbowPlot(cocl2tocis) # The standard deviation seems to really level off at 10
# Recluster with the appropriate number of dimensions
cocl2tocis <- FindNeighbors(cocl2tocis, dims = 1:10)
cocl2tocis <- FindClusters(cocl2tocis, resolution = 0.5)
cocl2tocis <- RunUMAP(cocl2tocis, dims = 1:10)
DimPlot(cocl2tocis, reduction = 'umap')
# Build list of lineages with at least 5 cells in dab tram and check what seurat cluster the cells are in
cocl2tocis_lin_clust_list <- list()
for (i in fivecell_cDNA$CoCl2toCis){
temp_df <- as.data.frame(table(Idents(cocl2tocis)[cocl2tocis$Lineage == i]))
colnames(temp_df) <- c('Seurat_clust', 'Num_cells')
temp_df$Pcnt_cells <- temp_df$Num_cells/sum(temp_df$Num_cells)
cocl2tocis_lin_clust_list[[i]] <- temp_df
}
# Need to do a random sampling of the same thing
cocl2tocis_lin_clust_rand_list <- list()
num_iter <- 800
for(j in 1:num_iter){
set.seed(j)
cocl2tocis_lin_clust_rand_list[[j]] <- list()
for (i in fivecell_cDNA$CoCl2toCis){
num_cells <- length(cocl2tocis$Lineage[cocl2tocis$Lineage == i])
temp_df <- as.data.frame(table(sample(Idents(cocl2tocis), num_cells, replace = F)))
colnames(temp_df) <- c('Seurat_clust', 'Num_cells')
temp_df$Pcnt_cells <- temp_df$Num_cells/sum(temp_df$Num_cells)
cocl2tocis_lin_clust_rand_list[[j]][[i]] <- temp_df
}
}Significance testing of the cocl2tocis simulation
# Find the mean of the maximum percentage of cells in a single cluster per lineage in each simulation
mean_cocl2tocis <- mean(unlist(lapply(cocl2tocis_lin_clust_list, function(x) max(x$Pcnt_cells))))
means_cocl2tocis_sim <- sapply(1:length(cocl2tocis_lin_clust_rand_list), function(y)
mean(unlist(lapply(cocl2tocis_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells)))))
z_mean_cocl2tocis <- (mean_cocl2tocis-mean(means_cocl2tocis_sim))/sd(means_cocl2tocis_sim)
pval_mean_cocl2tocis <- pnorm(z_mean_cocl2tocis, mean(means_cocl2tocis_sim), sd(means_cocl2tocis_sim), lower.tail = F)
# Find the weighted mean of the maximum percentage of cells in a single cluster per lineage in each simulation
weighted_mean_cocl2tocis <- weighted.mean(unlist(lapply(cocl2tocis_lin_clust_list, function(x) max(x$Pcnt_cells))),
unlist(lapply(cocl2tocis_lin_clust_list, function(x) sum(x$Num_cells))))
weighted_means_cocl2tocis_sim <- sapply(1:length(cocl2tocis_lin_clust_rand_list), function(y)
weighted.mean(unlist(lapply(cocl2tocis_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells))),
unlist(lapply(cocl2tocis_lin_clust_rand_list[[y]], function(x) sum(x$Num_cells)))))
z_wmean_cocl2tocis <- (weighted_mean_cocl2tocis-mean(weighted_means_cocl2tocis_sim))/sd(weighted_means_cocl2tocis_sim)
pval_wmean_cocl2tocis <- pnorm(z_wmean_cocl2tocis, mean(weighted_means_cocl2tocis_sim), sd(weighted_means_cocl2tocis_sim), lower.tail = F)
# Compare each individual distribution of maxes to the observed max by t test and track pval
ttest_pval_cocl2tocis <- c()
for (i in 1:length(means_cocl2tocis_sim)){
sim_maxes <- unlist(lapply(cocl2tocis_lin_clust_rand_list[[i]], function(x) max(x$Pcnt_cells)))
ttest_pval_cocl2tocis <- cbind(ttest_pval_cocl2tocis,t.test(x = unlist(lapply(cocl2tocis_lin_clust_list, function(x) max(x$Pcnt_cells))),y = unlist(lapply(cocl2tocis_lin_clust_rand_list[[i]], function(x) max(x$Pcnt_cells))), alternative = 'greater')$p.value)
}
# Save outputs
save(cocl2tocis_lin_clust_list, cocl2tocis_lin_clust_rand_list,z_mean_cocl2tocis,pval_mean_cocl2tocis, z_wmean_cocl2tocis, ttest_pval_cocl2tocis, file = '2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/cocl2tocis_sim_results.RData')
rm(cocl2tocis)Look at whether lineages cluster together in each individual condition - Starting with CoCl2toDabTram
Idents(all_data) <- all_data$OG_condition # Change the idents to the OG condition for subsetting to cocl2todabtram
cocl2todabtram <- subset(all_data, idents = 'cocl2todabtram') # Subset down to the cocl2todabtram object
ElbowPlot(cocl2todabtram) # The standard deviation seems to really level off at 10
# Recluster with the appropriate number of dimensions
cocl2todabtram <- FindNeighbors(cocl2todabtram, dims = 1:10)
cocl2todabtram <- FindClusters(cocl2todabtram, resolution = 0.5)
cocl2todabtram <- RunUMAP(cocl2todabtram, dims = 1:10)
DimPlot(cocl2todabtram, reduction = 'umap')
# Build list of lineages with at least 5 cells in dab tram and check what seurat cluster the cells are in
cocl2todabtram_lin_clust_list <- list()
for (i in fivecell_cDNA$CoCl2toDabTram){
temp_df <- as.data.frame(table(Idents(cocl2todabtram)[cocl2todabtram$Lineage == i]))
colnames(temp_df) <- c('Seurat_clust', 'Num_cells')
temp_df$Pcnt_cells <- temp_df$Num_cells/sum(temp_df$Num_cells)
cocl2todabtram_lin_clust_list[[i]] <- temp_df
}
# Need to do a random sampling of the same thing
cocl2todabtram_lin_clust_rand_list <- list()
num_iter <- 800
for(j in 1:num_iter){
set.seed(j)
cocl2todabtram_lin_clust_rand_list[[j]] <- list()
for (i in fivecell_cDNA$CoCl2toDabTram){
num_cells <- length(cocl2todabtram$Lineage[cocl2todabtram$Lineage == i])
temp_df <- as.data.frame(table(sample(Idents(cocl2todabtram), num_cells, replace = F)))
colnames(temp_df) <- c('Seurat_clust', 'Num_cells')
temp_df$Pcnt_cells <- temp_df$Num_cells/sum(temp_df$Num_cells)
cocl2todabtram_lin_clust_rand_list[[j]][[i]] <- temp_df
}
}Significance testing of the cocl2todabtram simulation
# Find the mean of the maximum percentage of cells in a single cluster per lineage in each simulation
mean_cocl2todabtram <- mean(unlist(lapply(cocl2todabtram_lin_clust_list, function(x) max(x$Pcnt_cells))))
means_cocl2todabtram_sim <- sapply(1:length(cocl2todabtram_lin_clust_rand_list), function(y)
mean(unlist(lapply(cocl2todabtram_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells)))))
z_mean_cocl2todabtram <- (mean_cocl2todabtram-mean(means_cocl2todabtram_sim))/sd(means_cocl2todabtram_sim)
pval_mean_cocl2todabtram <- pnorm(z_mean_cocl2todabtram, mean(means_cocl2todabtram_sim), sd(means_cocl2todabtram_sim), lower.tail = F)
# Find the weighted mean of the maximum percentage of cells in a single cluster per lineage in each simulation
weighted_mean_cocl2todabtram <- weighted.mean(unlist(lapply(cocl2todabtram_lin_clust_list, function(x) max(x$Pcnt_cells))),
unlist(lapply(cocl2todabtram_lin_clust_list, function(x) sum(x$Num_cells))))
weighted_means_cocl2todabtram_sim <- sapply(1:length(cocl2todabtram_lin_clust_rand_list), function(y)
weighted.mean(unlist(lapply(cocl2todabtram_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells))),
unlist(lapply(cocl2todabtram_lin_clust_rand_list[[y]], function(x) sum(x$Num_cells)))))
z_wmean_cocl2todabtram <- (weighted_mean_cocl2todabtram-mean(weighted_means_cocl2todabtram_sim))/sd(weighted_means_cocl2todabtram_sim)
pval_wmean_cocl2todabtram <- pnorm(z_wmean_cocl2todabtram, mean(weighted_means_cocl2todabtram_sim), sd(weighted_means_cocl2todabtram_sim), lower.tail = F)
# Compare each individual distribution of maxes to the observed max by t test and track pval
ttest_pval_cocl2todabtram <- c()
for (i in 1:length(means_cocl2todabtram_sim)){
sim_maxes <- unlist(lapply(cocl2todabtram_lin_clust_rand_list[[i]], function(x) max(x$Pcnt_cells)))
ttest_pval_cocl2todabtram <- cbind(ttest_pval_cocl2todabtram,t.test(x = unlist(lapply(cocl2todabtram_lin_clust_list, function(x) max(x$Pcnt_cells))),y = unlist(lapply(cocl2todabtram_lin_clust_rand_list[[i]], function(x) max(x$Pcnt_cells))), alternative = 'greater')$p.value)
}
# Save outputs
save(cocl2todabtram_lin_clust_list, cocl2todabtram_lin_clust_rand_list,z_mean_cocl2todabtram,pval_mean_cocl2todabtram, z_wmean_cocl2todabtram, ttest_pval_cocl2todabtram, file = '2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/cocl2todabtram_sim_results.RData')
rm(cocl2todabtram)Look at whether lineages cluster together in each individual condition - Starting with Cis
Idents(all_data) <- all_data$OG_condition # Change the idents to the OG condition for subsetting to cis
cis <- subset(all_data, idents = 'cis') # Subset down to the cis object
ElbowPlot(cis) # The standard deviation seems to really level off at 10
# Recluster with the appropriate number of dimensions
cis <- FindNeighbors(cis, dims = 1:10)
cis <- FindClusters(cis, resolution = 0.5)
cis <- RunUMAP(cis, dims = 1:10)
DimPlot(cis, reduction = 'umap')
# Build list of lineages with at least 5 cells in dab tram and check what seurat cluster the cells are in
cis_lin_clust_list <- list()
for (i in fivecell_cDNA$Cis){
temp_df <- as.data.frame(table(Idents(cis)[cis$Lineage == i]))
colnames(temp_df) <- c('Seurat_clust', 'Num_cells')
temp_df$Pcnt_cells <- temp_df$Num_cells/sum(temp_df$Num_cells)
cis_lin_clust_list[[i]] <- temp_df
}
# Need to do a random sampling of the same thing
cis_lin_clust_rand_list <- list()
num_iter <- 800
for(j in 1:num_iter){
set.seed(j)
cis_lin_clust_rand_list[[j]] <- list()
for (i in fivecell_cDNA$Cis){
num_cells <- length(cis$Lineage[cis$Lineage == i])
temp_df <- as.data.frame(table(sample(Idents(cis), num_cells, replace = F)))
colnames(temp_df) <- c('Seurat_clust', 'Num_cells')
temp_df$Pcnt_cells <- temp_df$Num_cells/sum(temp_df$Num_cells)
cis_lin_clust_rand_list[[j]][[i]] <- temp_df
}
}Significance testing of the cis simulation
# Find the mean of the maximum percentage of cells in a single cluster per lineage in each simulation
mean_cis <- mean(unlist(lapply(cis_lin_clust_list, function(x) max(x$Pcnt_cells))))
means_cis_sim <- sapply(1:length(cis_lin_clust_rand_list), function(y)
mean(unlist(lapply(cis_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells)))))
z_mean_cis <- (mean_cis-mean(means_cis_sim))/sd(means_cis_sim)
pval_mean_cis <- pnorm(z_mean_cis, mean(means_cis_sim), sd(means_cis_sim), lower.tail = F)
# Find the weighted mean of the maximum percentage of cells in a single cluster per lineage in each simulation
weighted_mean_cis <- weighted.mean(unlist(lapply(cis_lin_clust_list, function(x) max(x$Pcnt_cells))),
unlist(lapply(cis_lin_clust_list, function(x) sum(x$Num_cells))))
weighted_means_cis_sim <- sapply(1:length(cis_lin_clust_rand_list), function(y)
weighted.mean(unlist(lapply(cis_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells))),
unlist(lapply(cis_lin_clust_rand_list[[y]], function(x) sum(x$Num_cells)))))
z_wmean_cis <- (weighted_mean_cis-mean(weighted_means_cis_sim))/sd(weighted_means_cis_sim)
pval_wmean_cis <- pnorm(z_wmean_cis, mean(weighted_means_cis_sim), sd(weighted_means_cis_sim), lower.tail = F)
# Compare each individual distribution of maxes to the observed max by t test and track pval
ttest_pval_cis <- c()
for (i in 1:length(means_cis_sim)){
sim_maxes <- unlist(lapply(cis_lin_clust_rand_list[[i]], function(x) max(x$Pcnt_cells)))
ttest_pval_cis <- cbind(ttest_pval_cis,t.test(x = unlist(lapply(cis_lin_clust_list, function(x) max(x$Pcnt_cells))),y = unlist(lapply(cis_lin_clust_rand_list[[i]], function(x) max(x$Pcnt_cells))), alternative = 'greater')$p.value)
}
# Save outputs
save(cis_lin_clust_list, cis_lin_clust_rand_list,z_mean_cis,pval_mean_cis, z_wmean_cis, ttest_pval_cis, file = '2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/cis_sim_results.RData')
rm(cis)Look at whether lineages cluster together in each individual condition - Starting with cistocis
Idents(all_data) <- all_data$OG_condition # Change the idents to the OG condition for subsetting to cistocis
cistocis <- subset(all_data, idents = 'cistocis') # Subset down to the cistocis object
ElbowPlot(cistocis) # The standard deviation seems to really level off at 10
# Recluster with the appropriate number of dimensions
cistocis <- FindNeighbors(cistocis, dims = 1:10)
cistocis <- FindClusters(cistocis, resolution = 0.5)
cistocis <- RunUMAP(cistocis, dims = 1:10)
DimPlot(cistocis, reduction = 'umap')
# Build list of lineages with at least 5 cells in dab tram and check what seurat cluster the cells are in
cistocis_lin_clust_list <- list()
for (i in fivecell_cDNA$CistoCis){
temp_df <- as.data.frame(table(Idents(cistocis)[cistocis$Lineage == i]))
colnames(temp_df) <- c('Seurat_clust', 'Num_cells')
temp_df$Pcnt_cells <- temp_df$Num_cells/sum(temp_df$Num_cells)
cistocis_lin_clust_list[[i]] <- temp_df
}
# Need to do a random sampling of the same thing
cistocis_lin_clust_rand_list <- list()
num_iter <- 800
for(j in 1:num_iter){
set.seed(j)
cistocis_lin_clust_rand_list[[j]] <- list()
for (i in fivecell_cDNA$CistoCis){
num_cells <- length(cistocis$Lineage[cistocis$Lineage == i])
temp_df <- as.data.frame(table(sample(Idents(cistocis), num_cells, replace = F)))
colnames(temp_df) <- c('Seurat_clust', 'Num_cells')
temp_df$Pcnt_cells <- temp_df$Num_cells/sum(temp_df$Num_cells)
cistocis_lin_clust_rand_list[[j]][[i]] <- temp_df
}
}Significance testing of the cistocis simulation
# Find the mean of the maximum percentage of cells in a single cluster per lineage in each simulation
mean_cistocis <- mean(unlist(lapply(cistocis_lin_clust_list, function(x) max(x$Pcnt_cells))))
means_cistocis_sim <- sapply(1:length(cistocis_lin_clust_rand_list), function(y)
mean(unlist(lapply(cistocis_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells)))))
z_mean_cistocis <- (mean_cistocis-mean(means_cistocis_sim))/sd(means_cistocis_sim)
pval_mean_cistocis <- pnorm(z_mean_cistocis, mean(means_cistocis_sim), sd(means_cistocis_sim), lower.tail = F)
# Find the weighted mean of the maximum percentage of cells in a single cluster per lineage in each simulation
weighted_mean_cistocis <- weighted.mean(unlist(lapply(cistocis_lin_clust_list, function(x) max(x$Pcnt_cells))),
unlist(lapply(cistocis_lin_clust_list, function(x) sum(x$Num_cells))))
weighted_means_cistocis_sim <- sapply(1:length(cistocis_lin_clust_rand_list), function(y)
weighted.mean(unlist(lapply(cistocis_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells))),
unlist(lapply(cistocis_lin_clust_rand_list[[y]], function(x) sum(x$Num_cells)))))
z_wmean_cistocis <- (weighted_mean_cistocis-mean(weighted_means_cistocis_sim))/sd(weighted_means_cistocis_sim)
pval_wmean_cistocis <- pnorm(z_wmean_cistocis, mean(weighted_means_cistocis_sim), sd(weighted_means_cistocis_sim), lower.tail = F)
# Compare each individual distribution of maxes to the observed max by t test and track pval
ttest_pval_cistocis <- c()
for (i in 1:length(means_cistocis_sim)){
sim_maxes <- unlist(lapply(cistocis_lin_clust_rand_list[[i]], function(x) max(x$Pcnt_cells)))
ttest_pval_cistocis <- cbind(ttest_pval_cistocis,t.test(x = unlist(lapply(cistocis_lin_clust_list, function(x) max(x$Pcnt_cells))),y = unlist(lapply(cistocis_lin_clust_rand_list[[i]], function(x) max(x$Pcnt_cells))), alternative = 'greater')$p.value)
}
# Save outputs
save(cistocis_lin_clust_list, cistocis_lin_clust_rand_list,z_mean_cistocis,pval_mean_cistocis, z_wmean_cistocis, ttest_pval_cistocis, file = '2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/cistocis_sim_results.RData')
rm(cistocis)Look at whether lineages cluster together in each individual condition - Starting with cistodabtram
Idents(all_data) <- all_data$OG_condition # Change the idents to the OG condition for subsetting to cistodabtram
cistodabtram <- subset(all_data, idents = 'cistodabtram') # Subset down to the cistodabtram object
ElbowPlot(cistodabtram) # The standard deviation seems to really level off at 10
# Recluster with the appropriate number of dimensions
cistodabtram <- FindNeighbors(cistodabtram, dims = 1:10)
cistodabtram <- FindClusters(cistodabtram, resolution = 0.5)
cistodabtram <- RunUMAP(cistodabtram, dims = 1:10)
DimPlot(cistodabtram, reduction = 'umap')
# Build list of lineages with at least 5 cells in dab tram and check what seurat cluster the cells are in
cistodabtram_lin_clust_list <- list()
for (i in fivecell_cDNA$CistoDabTram){
temp_df <- as.data.frame(table(Idents(cistodabtram)[cistodabtram$Lineage == i]))
colnames(temp_df) <- c('Seurat_clust', 'Num_cells')
temp_df$Pcnt_cells <- temp_df$Num_cells/sum(temp_df$Num_cells)
cistodabtram_lin_clust_list[[i]] <- temp_df
}
# Need to do a random sampling of the same thing
cistodabtram_lin_clust_rand_list <- list()
num_iter <- 800
for(j in 1:num_iter){
set.seed(j)
cistodabtram_lin_clust_rand_list[[j]] <- list()
for (i in fivecell_cDNA$CistoDabTram){
num_cells <- length(cistodabtram$Lineage[cistodabtram$Lineage == i])
temp_df <- as.data.frame(table(sample(Idents(cistodabtram), num_cells, replace = F)))
colnames(temp_df) <- c('Seurat_clust', 'Num_cells')
temp_df$Pcnt_cells <- temp_df$Num_cells/sum(temp_df$Num_cells)
cistodabtram_lin_clust_rand_list[[j]][[i]] <- temp_df
}
}Significance testing of the cistodabtram simulation
# Find the mean of the maximum percentage of cells in a single cluster per lineage in each simulation
mean_cistodabtram <- mean(unlist(lapply(cistodabtram_lin_clust_list, function(x) max(x$Pcnt_cells))))
means_cistodabtram_sim <- sapply(1:length(cistodabtram_lin_clust_rand_list), function(y)
mean(unlist(lapply(cistodabtram_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells)))))
z_mean_cistodabtram <- (mean_cistodabtram-mean(means_cistodabtram_sim))/sd(means_cistodabtram_sim)
pval_mean_cistodabtram <- pnorm(z_mean_cistodabtram, mean(means_cistodabtram_sim), sd(means_cistodabtram_sim), lower.tail = F)
# Find the weighted mean of the maximum percentage of cells in a single cluster per lineage in each simulation
weighted_mean_cistodabtram <- weighted.mean(unlist(lapply(cistodabtram_lin_clust_list, function(x) max(x$Pcnt_cells))),
unlist(lapply(cistodabtram_lin_clust_list, function(x) sum(x$Num_cells))))
weighted_means_cistodabtram_sim <- sapply(1:length(cistodabtram_lin_clust_rand_list), function(y)
weighted.mean(unlist(lapply(cistodabtram_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells))),
unlist(lapply(cistodabtram_lin_clust_rand_list[[y]], function(x) sum(x$Num_cells)))))
z_wmean_cistodabtram <- (weighted_mean_cistodabtram-mean(weighted_means_cistodabtram_sim))/sd(weighted_means_cistodabtram_sim)
pval_wmean_cistodabtram <- pnorm(z_wmean_cistodabtram, mean(weighted_means_cistodabtram_sim), sd(weighted_means_cistodabtram_sim), lower.tail = F)
# Compare each individual distribution of maxes to the observed max by t test and track pval
ttest_pval_cistodabtram <- c()
for (i in 1:length(means_cistodabtram_sim)){
sim_maxes <- unlist(lapply(cistodabtram_lin_clust_rand_list[[i]], function(x) max(x$Pcnt_cells)))
ttest_pval_cistodabtram <- cbind(ttest_pval_cistodabtram,t.test(x = unlist(lapply(cistodabtram_lin_clust_list, function(x) max(x$Pcnt_cells))),y = unlist(lapply(cistodabtram_lin_clust_rand_list[[i]], function(x) max(x$Pcnt_cells))), alternative = 'greater')$p.value)
}
# Save outputs
save(cistodabtram_lin_clust_list, cistodabtram_lin_clust_rand_list,z_mean_cistodabtram,pval_mean_cistodabtram, z_wmean_cistodabtram, ttest_pval_cistodabtram, file = '2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/cistodabtram_sim_results.RData')
rm(cistodabtram)Look at whether lineages cluster together in each individual condition - Starting with cistococl2
Idents(all_data) <- all_data$OG_condition # Change the idents to the OG condition for subsetting to cistococl2
cistococl2 <- subset(all_data, idents = 'cistococl2') # Subset down to the cistococl2 object
ElbowPlot(cistococl2) # The standard deviation seems to really level off at 10
# Recluster with the appropriate number of dimensions
cistococl2 <- FindNeighbors(cistococl2, dims = 1:10)
cistococl2 <- FindClusters(cistococl2, resolution = 0.5)
cistococl2 <- RunUMAP(cistococl2, dims = 1:10)
DimPlot(cistococl2, reduction = 'umap')
# Build list of lineages with at least 5 cells in dab tram and check what seurat cluster the cells are in
cistococl2_lin_clust_list <- list()
for (i in fivecell_cDNA$CistoCoCl2){
temp_df <- as.data.frame(table(Idents(cistococl2)[cistococl2$Lineage == i]))
colnames(temp_df) <- c('Seurat_clust', 'Num_cells')
temp_df$Pcnt_cells <- temp_df$Num_cells/sum(temp_df$Num_cells)
cistococl2_lin_clust_list[[i]] <- temp_df
}
# Need to do a random sampling of the same thing
cistococl2_lin_clust_rand_list <- list()
num_iter <- 800
for(j in 1:num_iter){
set.seed(j)
cistococl2_lin_clust_rand_list[[j]] <- list()
for (i in fivecell_cDNA$CistoCoCl2){
num_cells <- length(cistococl2$Lineage[cistococl2$Lineage == i])
temp_df <- as.data.frame(table(sample(Idents(cistococl2), num_cells, replace = F)))
colnames(temp_df) <- c('Seurat_clust', 'Num_cells')
temp_df$Pcnt_cells <- temp_df$Num_cells/sum(temp_df$Num_cells)
cistococl2_lin_clust_rand_list[[j]][[i]] <- temp_df
}
}Significance testing of the cistococl2 simulation
# Find the mean of the maximum percentage of cells in a single cluster per lineage in each simulation
mean_cistococl2 <- mean(unlist(lapply(cistococl2_lin_clust_list, function(x) max(x$Pcnt_cells))))
means_cistococl2_sim <- sapply(1:length(cistococl2_lin_clust_rand_list), function(y)
mean(unlist(lapply(cistococl2_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells)))))
z_mean_cistococl2 <- (mean_cistococl2-mean(means_cistococl2_sim))/sd(means_cistococl2_sim)
pval_mean_cistococl2 <- pnorm(z_mean_cistococl2, mean(means_cistococl2_sim), sd(means_cistococl2_sim), lower.tail = F)
# Find the weighted mean of the maximum percentage of cells in a single cluster per lineage in each simulation
weighted_mean_cistococl2 <- weighted.mean(unlist(lapply(cistococl2_lin_clust_list, function(x) max(x$Pcnt_cells))),
unlist(lapply(cistococl2_lin_clust_list, function(x) sum(x$Num_cells))))
weighted_means_cistococl2_sim <- sapply(1:length(cistococl2_lin_clust_rand_list), function(y)
weighted.mean(unlist(lapply(cistococl2_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells))),
unlist(lapply(cistococl2_lin_clust_rand_list[[y]], function(x) sum(x$Num_cells)))))
z_wmean_cistococl2 <- (weighted_mean_cistococl2-mean(weighted_means_cistococl2_sim))/sd(weighted_means_cistococl2_sim)
pval_wmean_cistococl2 <- pnorm(z_wmean_cistococl2, mean(weighted_means_cistococl2_sim), sd(weighted_means_cistococl2_sim), lower.tail = F)
# Compare each individual distribution of maxes to the observed max by t test and track pval
ttest_pval_cistococl2 <- c()
for (i in 1:length(means_cistococl2_sim)){
sim_maxes <- unlist(lapply(cistococl2_lin_clust_rand_list[[i]], function(x) max(x$Pcnt_cells)))
ttest_pval_cistococl2 <- cbind(ttest_pval_cistococl2,t.test(x = unlist(lapply(cistococl2_lin_clust_list, function(x) max(x$Pcnt_cells))),y = unlist(lapply(cistococl2_lin_clust_rand_list[[i]], function(x) max(x$Pcnt_cells))), alternative = 'greater')$p.value)
}
# Save outputs
save(cistococl2_lin_clust_list, cistococl2_lin_clust_rand_list,z_mean_cistococl2,pval_mean_cistococl2, z_wmean_cistococl2, ttest_pval_cistococl2, file = '2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/cistococl2_sim_results.RData')
rm(cistococl2)Build dataframe of overall simulation results
sim_results <- rbind(
dabtram <- data.frame("Mean_Zscore" = z_mean_dabtram, "Mean_pval" = pval_mean_dabtram, "Weighted_Mean_Zscore" = z_wmean_dabtram, "Weighted_Mean_pval" = pval_wmean_dabtram, ttest_sims_sig = sum(ttest_pval_dabtram <= 0.05)),
dabtramtodabtram <- data.frame("Mean_Zscore" = z_mean_dabtramtodabtram, "Mean_pval" = pval_mean_dabtramtodabtram, "Weighted_Mean_Zscore" = z_wmean_dabtramtodabtram, "Weighted_Mean_pval" = pval_wmean_dabtramtodabtram, ttest_sims_sig = sum(ttest_pval_dabtramtodabtram <= 0.05)),
dabtramtococl2 <- data.frame("Mean_Zscore" = z_mean_dabtramtococl2, "Mean_pval" = pval_mean_dabtramtococl2, "Weighted_Mean_Zscore" = z_wmean_dabtramtococl2, "Weighted_Mean_pval" = pval_wmean_dabtramtococl2, ttest_sims_sig = sum(ttest_pval_dabtramtococl2 <= 0.05)),
dabtramtocis <- data.frame("Mean_Zscore" = z_mean_dabtramtocis, "Mean_pval" = pval_mean_dabtramtocis, "Weighted_Mean_Zscore" = z_wmean_dabtramtocis, "Weighted_Mean_pval" = pval_wmean_dabtramtocis, ttest_sims_sig = sum(ttest_pval_dabtramtocis <= 0.05)),
cis <- data.frame("Mean_Zscore" = z_mean_cis, "Mean_pval" = pval_mean_cis, "Weighted_Mean_Zscore" = z_wmean_cis, "Weighted_Mean_pval" = pval_wmean_cis, ttest_sims_sig = sum(ttest_pval_cis <= 0.05)),
cistodabtram <- data.frame("Mean_Zscore" = z_mean_cistodabtram, "Mean_pval" = pval_mean_cistodabtram, "Weighted_Mean_Zscore" = z_wmean_cistodabtram, "Weighted_Mean_pval" = pval_wmean_cistodabtram, ttest_sims_sig = sum(ttest_pval_cistodabtram <= 0.05)),
cistococl2 <- data.frame("Mean_Zscore" = z_mean_cistococl2, "Mean_pval" = pval_mean_cistococl2, "Weighted_Mean_Zscore" = z_wmean_cistococl2, "Weighted_Mean_pval" = pval_wmean_cistococl2, ttest_sims_sig = sum(ttest_pval_cistococl2 <= 0.05)),
cistocis <- data.frame("Mean_Zscore" = z_mean_cistocis, "Mean_pval" = pval_mean_cistocis, "Weighted_Mean_Zscore" = z_wmean_cistocis, "Weighted_Mean_pval" = pval_wmean_cistocis, ttest_sims_sig = sum(ttest_pval_cistocis <= 0.05)),
cocl2 <- data.frame("Mean_Zscore" = z_mean_cocl2, "Mean_pval" = pval_mean_cocl2, "Weighted_Mean_Zscore" = z_wmean_cocl2, "Weighted_Mean_pval" = pval_wmean_cocl2, ttest_sims_sig = sum(ttest_pval_cocl2 <= 0.05)),
cocl2todabtram <- data.frame("Mean_Zscore" = z_mean_cocl2todabtram, "Mean_pval" = pval_mean_cocl2todabtram, "Weighted_Mean_Zscore" = z_wmean_cocl2todabtram, "Weighted_Mean_pval" = pval_wmean_cocl2todabtram, ttest_sims_sig = sum(ttest_pval_cocl2todabtram <= 0.05)),
cocl2tococl2 <- data.frame("Mean_Zscore" = z_mean_cocl2tococl2, "Mean_pval" = pval_mean_cocl2tococl2, "Weighted_Mean_Zscore" = z_wmean_cocl2tococl2, "Weighted_Mean_pval" = pval_wmean_cocl2tococl2, ttest_sims_sig = sum(ttest_pval_cocl2tococl2 <= 0.05)),
cocl2tocis <- data.frame("Mean_Zscore" = z_mean_cocl2tocis, "Mean_pval" = pval_mean_cocl2tocis, "Weighted_Mean_Zscore" = z_wmean_cocl2tocis, "Weighted_Mean_pval" = pval_wmean_cocl2tocis, ttest_sims_sig = sum(ttest_pval_cocl2tocis <= 0.05))
)
rownames(sim_results) <- c("dabtram", "dabtramtodabtram", "dabtramtococl2", "dabtramtocis", "cis", "cistodabtram", "cistococl2", "cistocis", "cocl2", "cocl2todabtram", "cocl2tococl2", "cocl2tocis")
write.csv(sim_results, '2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/sim_results.csv', row.names = T) Load simulation data back in
sim_results <- read.csv('2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/sim_results.csv')
load('2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/dabtram_sim_results.RData')
load('2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/dabtramtodabtram_sim_results.RData')
load('2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/dabtramtococl2_sim_results.RData')
load('2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/dabtramtocis_sim_results.RData')
load('2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/cocl2_sim_results.RData')
load('2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/cocl2todabtram_sim_results.RData')
load('2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/cocl2tococl2_sim_results.RData')
load('2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/cocl2tocis_sim_results.RData')
load('2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/cis_sim_results.RData')
load('2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/cistodabtram_sim_results.RData')
load('2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/cistococl2_sim_results.RData')
load('2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/cistocis_sim_results.RData')
Plot heatmaps of true percentage of cells per lineage in each cluster vs random simulations
breaks <- seq(0, 1, 0.1)
# DabTram
pdf('2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/proportion_clusters_heatmaps.pdf', width = 16)
dabtram_pcnts <- as.data.frame(sapply(dabtram_lin_clust_list, '[[', 3))
cell_nums <- sapply(dabtram_lin_clust_list, function(x) sum(x$Num_cells))
rownames(dabtram_pcnts) <- c("0","1","2","3","4","5","6")
colnames(dabtram_pcnts) <- paste(names(cell_nums), rep('-',length(cell_nums)), cell_nums, rep('cells', length(cell_nums)))
p <- pheatmap(dabtram_pcnts,cluster_rows = F, color = inferno(11), main = "DabTram", breaks = breaks) #
p
dabtram_pcnts_rand <- as.data.frame(Reduce(`+`, lapply(dabtram_lin_clust_rand_list, function(x) {
sapply(x, function(y) y$Pcnt_cells)
}))/ length(dabtram_lin_clust_rand_list))
rownames(dabtram_pcnts_rand) <- c("0","1","2","3","4","5","6")
colnames(dabtram_pcnts_rand) <- paste(names(cell_nums), rep('-',length(cell_nums)), cell_nums, rep('cells', length(cell_nums)))
dabtram_pcnts_rand <- dabtram_pcnts_rand[,p$tree_col$order]
pheatmap(dabtram_pcnts_rand,cluster_rows = F, color = inferno(11), main = "DabTram Rand", breaks = breaks, cluster_cols = F)
# DabTram to DabTram
dabtramtodabtram_pcnts <- as.data.frame(sapply(dabtramtodabtram_lin_clust_list, '[[', 3))
cell_nums <- sapply(dabtramtodabtram_lin_clust_list, function(x) sum(x$Num_cells))
rownames(dabtramtodabtram_pcnts) <- c("0","1","2","3","4","5","6")
colnames(dabtramtodabtram_pcnts) <- paste(names(cell_nums), rep('-',length(cell_nums)), cell_nums, rep('cells', length(cell_nums)))
p <- pheatmap(dabtramtodabtram_pcnts,cluster_rows = F, color = inferno(11), main = "DabTram to DabTram", breaks = breaks)
p
dabtramtodabtram_pcnts_rand <- as.data.frame(Reduce(`+`, lapply(dabtramtodabtram_lin_clust_rand_list, function(x) {
sapply(x, function(y) y$Pcnt_cells)
}))/ length(dabtramtodabtram_lin_clust_rand_list))
rownames(dabtramtodabtram_pcnts_rand) <- c("0","1","2","3","4","5","6")
colnames(dabtramtodabtram_pcnts_rand) <- paste(names(cell_nums), rep('-',length(cell_nums)), cell_nums, rep('cells', length(cell_nums)))
dabtramtodabtram_pcnts_rand <- dabtramtodabtram_pcnts_rand[,p$tree_col$order]
pheatmap(dabtramtodabtram_pcnts_rand,cluster_rows = F, color = inferno(11), main = "DabTram to DabTram Rand", breaks = breaks, cluster_cols = F)
# DabTram to CoCl2
dabtramtococl2_pcnts <- as.data.frame(sapply(dabtramtococl2_lin_clust_list, '[[', 3))
cell_nums <- sapply(dabtramtococl2_lin_clust_list, function(x) sum(x$Num_cells))
rownames(dabtramtococl2_pcnts) <- c("0","1","2","3","4","5","6","7")
colnames(dabtramtococl2_pcnts) <- paste(names(cell_nums), rep('-',length(cell_nums)), cell_nums, rep('cells', length(cell_nums)))
p <- pheatmap(dabtramtococl2_pcnts,cluster_rows = F, color = inferno(11), main = "DabTram to CoCl2", breaks = breaks)
p
dabtramtococl2_pcnts_rand <- as.data.frame(Reduce(`+`, lapply(dabtramtococl2_lin_clust_rand_list, function(x) {
sapply(x, function(y) y$Pcnt_cells)
}))/ length(dabtramtococl2_lin_clust_rand_list))
rownames(dabtramtococl2_pcnts_rand) <- c("0","1","2","3","4","5","6","7")
colnames(dabtramtococl2_pcnts_rand) <- paste(names(cell_nums), rep('-',length(cell_nums)), cell_nums, rep('cells', length(cell_nums)))
dabtramtococl2_pcnts_rand <- dabtramtococl2_pcnts_rand[,p$tree_col$order]
pheatmap(dabtramtococl2_pcnts_rand,cluster_rows = F, color = inferno(11), main = "DabTram to CoCl2 Rand", breaks = breaks, cluster_cols = F)
# DabTram to Cis
dabtramtocis_pcnts <- as.data.frame(sapply(dabtramtocis_lin_clust_list, '[[', 3))
cell_nums <- sapply(dabtramtocis_lin_clust_list, function(x) sum(x$Num_cells))
rownames(dabtramtocis_pcnts) <- c("0","1","2","3","4","5","6")
colnames(dabtramtocis_pcnts) <- paste(names(cell_nums), rep('-',length(cell_nums)), cell_nums, rep('cells', length(cell_nums)))
p <- pheatmap(dabtramtocis_pcnts,cluster_rows = F, color = inferno(11), main = "DabTram to Cis", breaks = breaks)
p
dabtramtocis_pcnts_rand <- as.data.frame(Reduce(`+`, lapply(dabtramtocis_lin_clust_rand_list, function(x) {
sapply(x, function(y) y$Pcnt_cells)
}))/ length(dabtramtocis_lin_clust_rand_list))
rownames(dabtramtocis_pcnts_rand) <- c("0","1","2","3","4","5","6")
colnames(dabtramtocis_pcnts_rand) <- paste(names(cell_nums), rep('-',length(cell_nums)), cell_nums, rep('cells', length(cell_nums)))
dabtramtocis_pcnts_rand <- dabtramtocis_pcnts_rand[,p$tree_col$order]
pheatmap(dabtramtocis_pcnts_rand,cluster_rows = F, color = inferno(11), main = "DabTram to Cis Rand", breaks = breaks, cluster_cols = F)
# CoCl2 objects
cocl2_pcnts <- as.data.frame(sapply(cocl2_lin_clust_list, '[[', 3))
cell_nums <- sapply(cocl2_lin_clust_list, function(x) sum(x$Num_cells))
rownames(cocl2_pcnts) <- c("0","1","2","3","4","5","6","7","8")
colnames(cocl2_pcnts) <- paste(names(cell_nums), rep('-',length(cell_nums)), cell_nums, rep('cells', length(cell_nums)))
p <- pheatmap(cocl2_pcnts,cluster_rows = F, color = inferno(11), main = "CoCl2", breaks = breaks)
p
cocl2_pcnts_rand <- as.data.frame(Reduce(`+`, lapply(cocl2_lin_clust_rand_list, function(x) {
sapply(x, function(y) y$Pcnt_cells)
}))/ length(cocl2_lin_clust_rand_list))
rownames(cocl2_pcnts_rand) <- c("0","1","2","3","4","5","6","7","8")
colnames(cocl2_pcnts_rand) <- paste(names(cell_nums), rep('-',length(cell_nums)), cell_nums, rep('cells', length(cell_nums)))
cocl2_pcnts_rand <- cocl2_pcnts_rand[,p$tree_col$order]
pheatmap(cocl2_pcnts_rand,cluster_rows = F, color = inferno(11), main = "CoCl2 Rand", breaks = breaks, cluster_cols = F)
# CoCl2 to DabTram
cocl2todabtram_pcnts <- as.data.frame(sapply(cocl2todabtram_lin_clust_list, '[[', 3))
cell_nums <- sapply(cocl2todabtram_lin_clust_list, function(x) sum(x$Num_cells))
rownames(cocl2todabtram_pcnts) <- c("0","1","2","3","4","5","6")
colnames(cocl2todabtram_pcnts) <- paste(names(cell_nums), rep('-',length(cell_nums)), cell_nums, rep('cells', length(cell_nums)))
p <- pheatmap(cocl2todabtram_pcnts,cluster_rows = F, color = inferno(11), main = "CoCl2 to DabTram", breaks = breaks)
p
cocl2todabtram_pcnts_rand <- as.data.frame(Reduce(`+`, lapply(cocl2todabtram_lin_clust_rand_list, function(x) {
sapply(x, function(y) y$Pcnt_cells)
}))/ length(cocl2todabtram_lin_clust_rand_list))
rownames(cocl2todabtram_pcnts_rand) <- c("0","1","2","3","4","5","6")
colnames(cocl2todabtram_pcnts_rand) <- paste(names(cell_nums), rep('-',length(cell_nums)), cell_nums, rep('cells', length(cell_nums)))
cocl2todabtram_pcnts_rand <- cocl2todabtram_pcnts_rand[,p$tree_col$order]
pheatmap(cocl2todabtram_pcnts_rand,cluster_rows = F, color = inferno(11), main = "CoCl2 to DabTram Rand", breaks = breaks, cluster_cols = F)
# CoCl2 to CoCl2
cocl2tococl2_pcnts <- as.data.frame(sapply(cocl2tococl2_lin_clust_list, '[[', 3))
cell_nums <- sapply(cocl2tococl2_lin_clust_list, function(x) sum(x$Num_cells))
rownames(cocl2tococl2_pcnts) <- c("0","1","2","3","4","5")
colnames(cocl2tococl2_pcnts) <- paste(names(cell_nums), rep('-',length(cell_nums)), cell_nums, rep('cells', length(cell_nums)))
p <- pheatmap(cocl2tococl2_pcnts,cluster_rows = F, color = inferno(11), main = "CoCl2 to CoCl2", breaks = breaks)
p
cocl2tococl2_pcnts_rand <- as.data.frame(Reduce(`+`, lapply(cocl2tococl2_lin_clust_rand_list, function(x) {
sapply(x, function(y) y$Pcnt_cells)
}))/ length(cocl2tococl2_lin_clust_rand_list))
rownames(cocl2tococl2_pcnts_rand) <- c("0","1","2","3","4","5")
colnames(cocl2tococl2_pcnts_rand) <- paste(names(cell_nums), rep('-',length(cell_nums)), cell_nums, rep('cells', length(cell_nums)))
cocl2tococl2_pcnts_rand <- cocl2tococl2_pcnts_rand[,p$tree_col$order]
pheatmap(cocl2tococl2_pcnts_rand,cluster_rows = F, color = inferno(11), main = "CoCl2 to CoCl2 Rand", breaks = breaks, cluster_cols = F)
# CoCl2 to Cis
cocl2tocis_pcnts <- as.data.frame(sapply(cocl2tocis_lin_clust_list, '[[', 3))
cell_nums <- sapply(cocl2tocis_lin_clust_list, function(x) sum(x$Num_cells))
rownames(cocl2tocis_pcnts) <- c("0","1","2","3","4","5")
colnames(cocl2tocis_pcnts) <- paste(names(cell_nums), rep('-',length(cell_nums)), cell_nums, rep('cells', length(cell_nums)))
p <- pheatmap(cocl2tocis_pcnts,cluster_rows = F, color = inferno(11), main = "CoCl2 to Cis", breaks = breaks)
p
cocl2tocis_pcnts_rand <- as.data.frame(Reduce(`+`, lapply(cocl2tocis_lin_clust_rand_list, function(x) {
sapply(x, function(y) y$Pcnt_cells)
}))/ length(cocl2tocis_lin_clust_rand_list))
rownames(cocl2tocis_pcnts_rand) <- c("0","1","2","3","4","5")
colnames(cocl2tocis_pcnts_rand) <- paste(names(cell_nums), rep('-',length(cell_nums)), cell_nums, rep('cells', length(cell_nums)))
cocl2tocis_pcnts_rand <- cocl2tocis_pcnts_rand[,p$tree_col$order]
pheatmap(cocl2tocis_pcnts_rand,cluster_rows = F, color = inferno(11), main = "CoCl2 to Cis Rand", breaks = breaks, cluster_cols = F)
# Cis
cis_pcnts <- as.data.frame(sapply(cis_lin_clust_list, '[[', 3))
cell_nums <- sapply(cis_lin_clust_list, function(x) sum(x$Num_cells))
rownames(cis_pcnts) <- c("0","1","2","3","4","5","6","7")
colnames(cis_pcnts) <- paste(names(cell_nums), rep('-',length(cell_nums)), cell_nums, rep('cells', length(cell_nums)))
p <- pheatmap(cis_pcnts,cluster_rows = F, color = inferno(10), main = "Cis", breaks = breaks)
p
cis_pcnts_rand <- as.data.frame(Reduce(`+`, lapply(cis_lin_clust_rand_list, function(x) {
sapply(x, function(y) y$Pcnt_cells)
}))/ length(cis_lin_clust_rand_list))
rownames(cis_pcnts_rand) <- c("0","1","2","3","4","5","6","7")
colnames(cis_pcnts_rand) <- paste(names(cell_nums), rep('-',length(cell_nums)), cell_nums, rep('cells', length(cell_nums)))
cis_pcnts_rand <- cis_pcnts_rand[,p$tree_col$order]
pheatmap(cis_pcnts_rand,cluster_rows = F, color = inferno(11), main = "Cis Rand", breaks = breaks, cluster_cols = F)
# Cis to DabTram
cistodabtram_pcnts <- as.data.frame(sapply(cistodabtram_lin_clust_list, '[[', 3))
cell_nums <- sapply(cistodabtram_lin_clust_list, function(x) sum(x$Num_cells))
rownames(cistodabtram_pcnts) <- c("0","1","2","3","4","5","6","7")
colnames(cistodabtram_pcnts) <- paste(names(cell_nums), rep('-',length(cell_nums)), cell_nums, rep('cells', length(cell_nums)))
p <- pheatmap(cistodabtram_pcnts,cluster_rows = F, color = inferno(11), main = "Cis to DabTram", breaks = breaks)
p
cistodabtram_pcnts_rand <- as.data.frame(Reduce(`+`, lapply(cistodabtram_lin_clust_rand_list, function(x) {
sapply(x, function(y) y$Pcnt_cells)
}))/ length(cistodabtram_lin_clust_rand_list))
rownames(cistodabtram_pcnts_rand) <- c("0","1","2","3","4","5","6","7")
colnames(cistodabtram_pcnts_rand) <- paste(names(cell_nums), rep('-',length(cell_nums)), cell_nums, rep('cells', length(cell_nums)))
cistodabtram_pcnts_rand <- cistodabtram_pcnts_rand[,p$tree_col$order]
pheatmap(cistodabtram_pcnts_rand,cluster_rows = F, color = inferno(11), main = "Cis to DabTram Rand", breaks = breaks, cluster_cols = F)
# Cis to CoCl2
cistococl2_pcnts <- as.data.frame(sapply(cistococl2_lin_clust_list, '[[', 3))
cell_nums <- sapply(cistococl2_lin_clust_list, function(x) sum(x$Num_cells))
rownames(cistococl2_pcnts) <- c("0","1","2","3","4","5","6")
colnames(cistococl2_pcnts) <- paste(names(cell_nums), rep('-',length(cell_nums)), cell_nums, rep('cells', length(cell_nums)))
p <- pheatmap(cistococl2_pcnts,cluster_rows = F, color = inferno(11), main = "Cis to CoCl2", breaks = breaks)
p
cistococl2_pcnts_rand <- as.data.frame(Reduce(`+`, lapply(cistococl2_lin_clust_rand_list, function(x) {
sapply(x, function(y) y$Pcnt_cells)
}))/ length(cistococl2_lin_clust_rand_list))
rownames(cistococl2_pcnts_rand) <- c("0","1","2","3","4","5","6")
colnames(cistococl2_pcnts_rand) <- paste(names(cell_nums), rep('-',length(cell_nums)), cell_nums, rep('cells', length(cell_nums)))
cistococl2_pcnts_rand <- cistococl2_pcnts_rand[,p$tree_col$order]
pheatmap(cistococl2_pcnts_rand,cluster_rows = F, color = inferno(11), main = "Cis to CoCl2 Rand", breaks = breaks, cluster_cols = F)
# Cis to Cis
cistocis_pcnts <- as.data.frame(sapply(cistocis_lin_clust_list, '[[', 3))
cell_nums <- sapply(cistocis_lin_clust_list, function(x) sum(x$Num_cells))
rownames(cistocis_pcnts) <- c("0","1","2","3","4","5","6","7","8")
colnames(cistocis_pcnts) <- paste(names(cell_nums), rep('-',length(cell_nums)), cell_nums, rep('cells', length(cell_nums)))
p <- pheatmap(cistocis_pcnts,cluster_rows = F, color = inferno(11), main = "Cis to Cis", breaks = breaks)
p
cistocis_pcnts_rand <- as.data.frame(Reduce(`+`, lapply(cistocis_lin_clust_rand_list, function(x) {
sapply(x, function(y) y$Pcnt_cells)
}))/ length(cistocis_lin_clust_rand_list))
rownames(cistocis_pcnts_rand) <- c("0","1","2","3","4","5","6","7","8")
colnames(cistocis_pcnts_rand) <- paste(names(cell_nums), rep('-',length(cell_nums)), cell_nums, rep('cells', length(cell_nums)))
cistocis_pcnts_rand <- cistocis_pcnts_rand[,p$tree_col$order]
pheatmap(cistocis_pcnts_rand,cluster_rows = F, color = inferno(11), main = "Cis to Cis Rand", breaks = breaks, cluster_cols = F)
dev.off()
plot the different test statistics - histogram of weighted means of maximum contribution to lineage vs true
pdf('2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/weighted_mean_cluster_assignments_test_stats.pdf')
# DabTram
# Find the weighted mean of the maximum percentage of cells in a single cluster per lineage in each simulation
wmean <- weighted.mean(unlist(lapply(dabtram_lin_clust_list, function(x) max(x$Pcnt_cells))),
unlist(lapply(dabtram_lin_clust_list, function(x) sum(x$Num_cells))))
df <- data.frame(weighted_mean = sapply(1:length(dabtram_lin_clust_rand_list), function(y)
weighted.mean(unlist(lapply(dabtram_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells))),
unlist(lapply(dabtram_lin_clust_rand_list[[y]], function(x) sum(x$Num_cells))))))
ggplot(df, aes(x = weighted_mean))+geom_histogram(fill = '#623594', color = 'black', binwidth = 0.005) + geom_vline(xintercept = wmean, linetype = 'dashed', color = 'red') + geom_text(aes(x = wmean+.03, label = round(wmean, 3), y = 500), color = 'red') + labs(y = 'Number of simulations', x = 'Weighted mean of percent of cells in dominant lineage', title = 'DabTram') + xlim(0.15, 0.65) + ylim(0,850) + geom_text(aes(x = wmean+.03, label = 'Observed', y = 525), color = 'red') + theme_classic()
# DabTram to DabTram
# Find the weighted mean of the maximum percentage of cells in a single cluster per lineage in each simulation
wmean <- weighted.mean(unlist(lapply(dabtramtodabtram_lin_clust_list, function(x) max(x$Pcnt_cells))),
unlist(lapply(dabtramtodabtram_lin_clust_list, function(x) sum(x$Num_cells))))
df <- data.frame(weighted_mean = sapply(1:length(dabtramtodabtram_lin_clust_rand_list), function(y)
weighted.mean(unlist(lapply(dabtramtodabtram_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells))),
unlist(lapply(dabtramtodabtram_lin_clust_rand_list[[y]], function(x) sum(x$Num_cells))))))
ggplot(df, aes(x = weighted_mean))+geom_histogram(fill = '#561e59', color = 'black', binwidth = 0.005) + geom_vline(xintercept = wmean, linetype = 'dashed', color = 'red') + geom_text(aes(x = wmean+.03, label = round(wmean, 3), y = 500), color = 'red') + labs(y = 'Number of simulations', x = 'Weighted mean of percent of cells in dominant lineage', title = 'DabTram to DabTram') + xlim(0.15, 0.65) + ylim(0,850) + geom_text(aes(x = wmean+.03, label = 'Observed', y = 525), color = 'red') + theme_classic()
# DabTram to CoCl2
# Find the weighted mean of the maximum percentage of cells in a single cluster per lineage in each simulation
wmean <- weighted.mean(unlist(lapply(dabtramtococl2_lin_clust_list, function(x) max(x$Pcnt_cells))),
unlist(lapply(dabtramtococl2_lin_clust_list, function(x) sum(x$Num_cells))))
df <- data.frame(weighted_mean = sapply(1:length(dabtramtococl2_lin_clust_rand_list), function(y)
weighted.mean(unlist(lapply(dabtramtococl2_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells))),
unlist(lapply(dabtramtococl2_lin_clust_rand_list[[y]], function(x) sum(x$Num_cells))))))
ggplot(df, aes(x = weighted_mean))+geom_histogram(fill = '#A2248E', color = 'black', binwidth = 0.005) + geom_vline(xintercept = wmean, linetype = 'dashed', color = 'red') + geom_text(aes(x = wmean+.03, label = round(wmean, 3), y = 500), color = 'red') + labs(y = 'Number of simulations', x = 'Weighted mean of percent of cells in dominant lineage', title = 'DabTram to CoCl2') + xlim(0.15, 0.65) + ylim(0,850) + geom_text(aes(x = wmean+.03, label = 'Observed', y = 525), color = 'red') + theme_classic()
# DabTram to Cis
# Find the weighted mean of the maximum percentage of cells in a single cluster per lineage in each simulation
wmean <- weighted.mean(unlist(lapply(dabtramtocis_lin_clust_list, function(x) max(x$Pcnt_cells))),
unlist(lapply(dabtramtocis_lin_clust_list, function(x) sum(x$Num_cells))))
df <- data.frame(weighted_mean = sapply(1:length(dabtramtocis_lin_clust_rand_list), function(y)
weighted.mean(unlist(lapply(dabtramtocis_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells))),
unlist(lapply(dabtramtocis_lin_clust_rand_list[[y]], function(x) sum(x$Num_cells))))))
ggplot(df, aes(x = weighted_mean))+geom_histogram(fill = '#9D85BE', color = 'black', binwidth = 0.005) + geom_vline(xintercept = wmean, linetype = 'dashed', color = 'red') + geom_text(aes(x = wmean+.03, label = round(wmean, 3), y = 500), color = 'red') + labs(y = 'Number of simulations', x = 'Weighted mean of percent of cells in dominant lineage', title = 'DabTram to Cis') + xlim(0.15, 0.65) + ylim(0,850) + geom_text(aes(x = wmean+.03, label = 'Observed', y = 525), color = 'red') + theme_classic()
# CoCl2
# Find the weighted mean of the maximum percentage of cells in a single cluster per lineage in each simulation
wmean <- weighted.mean(unlist(lapply(cocl2_lin_clust_list, function(x) max(x$Pcnt_cells))),
unlist(lapply(cocl2_lin_clust_list, function(x) sum(x$Num_cells))))
df <- data.frame(weighted_mean = sapply(1:length(cocl2_lin_clust_rand_list), function(y)
weighted.mean(unlist(lapply(cocl2_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells))),
unlist(lapply(cocl2_lin_clust_rand_list[[y]], function(x) sum(x$Num_cells))))))
ggplot(df, aes(x = weighted_mean))+geom_histogram(fill = '#0F8241', color = 'black', binwidth = 0.005) + geom_vline(xintercept = wmean, linetype = 'dashed', color = 'red') + geom_text(aes(x = wmean+.03, label = round(wmean, 3), y = 500), color = 'red') + labs(y = 'Number of simulations', x = 'Weighted mean of percent of cells in dominant lineage', title = 'CoCl2') + xlim(0.15, 0.65) + ylim(0,850) + geom_text(aes(x = wmean+.03, label = 'Observed', y = 525), color = 'red') + theme_classic()
# CoCl2 to DabTram
# Find the weighted mean of the maximum percentage of cells in a single cluster per lineage in each simulation
wmean <- weighted.mean(unlist(lapply(cocl2todabtram_lin_clust_list, function(x) max(x$Pcnt_cells))),
unlist(lapply(cocl2todabtram_lin_clust_list, function(x) sum(x$Num_cells))))
df <- data.frame(weighted_mean = sapply(1:length(cocl2todabtram_lin_clust_rand_list), function(y)
weighted.mean(unlist(lapply(cocl2todabtram_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells))),
unlist(lapply(cocl2todabtram_lin_clust_rand_list[[y]], function(x) sum(x$Num_cells))))))
ggplot(df, aes(x = weighted_mean))+geom_histogram(fill = '#10413B', color = 'black', binwidth = 0.005) + geom_vline(xintercept = wmean, linetype = 'dashed', color = 'red') + geom_text(aes(x = wmean+.03, label = round(wmean, 3), y = 500), color = 'red') + labs(y = 'Number of simulations', x = 'Weighted mean of percent of cells in dominant lineage', title = 'CoCl2 to DabTram') + xlim(0.15, 0.65) + ylim(0,850) + geom_text(aes(x = wmean+.03, label = 'Observed', y = 525), color = 'red') + theme_classic()
# CoCl2 to CoCl2
# Find the weighted mean of the maximum percentage of cells in a single cluster per lineage in each simulation
wmean <- weighted.mean(unlist(lapply(cocl2tococl2_lin_clust_list, function(x) max(x$Pcnt_cells))),
unlist(lapply(cocl2tococl2_lin_clust_list, function(x) sum(x$Num_cells))))
df <- data.frame(weighted_mean = sapply(1:length(cocl2tococl2_lin_clust_rand_list), function(y)
weighted.mean(unlist(lapply(cocl2tococl2_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells))),
unlist(lapply(cocl2tococl2_lin_clust_rand_list[[y]], function(x) sum(x$Num_cells))))))
ggplot(df, aes(x = weighted_mean))+geom_histogram(fill = '#6ABD45', color = 'black', binwidth = 0.005) + geom_vline(xintercept = wmean, linetype = 'dashed', color = 'red') + geom_text(aes(x = wmean+.03, label = round(wmean, 3), y = 500), color = 'red') + labs(y = 'Number of simulations', x = 'Weighted mean of percent of cells in dominant lineage', title = 'CoCl2 to CoCl2') + xlim(0.15, 0.65) + ylim(0,850) + geom_text(aes(x = wmean+.03, label = 'Observed', y = 525), color = 'red') + theme_classic()
# CoCl2 to Cis
# Find the weighted mean of the maximum percentage of cells in a single cluster per lineage in each simulation
wmean <- weighted.mean(unlist(lapply(cocl2tocis_lin_clust_list, function(x) max(x$Pcnt_cells))),
unlist(lapply(cocl2tocis_lin_clust_list, function(x) sum(x$Num_cells))))
df <- data.frame(weighted_mean = sapply(1:length(cocl2tocis_lin_clust_rand_list), function(y)
weighted.mean(unlist(lapply(cocl2tocis_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells))),
unlist(lapply(cocl2tocis_lin_clust_rand_list[[y]], function(x) sum(x$Num_cells))))))
ggplot(df, aes(x = weighted_mean))+geom_histogram(fill = '#6DC49C', color = 'black', binwidth = 0.005) + geom_vline(xintercept = wmean, linetype = 'dashed', color = 'red') + geom_text(aes(x = wmean+.03, label = round(wmean, 3), y = 500), color = 'red') + labs(y = 'Number of simulations', x = 'Weighted mean of percent of cells in dominant lineage', title = 'CoCl2 to Cis') + xlim(0.15, 0.65) + ylim(0,850) + geom_text(aes(x = wmean+.03, label = 'Observed', y = 525), color = 'red') + theme_classic()
# Cis
# Find the weighted mean of the maximum percentage of cells in a single cluster per lineage in each simulation
wmean <- weighted.mean(unlist(lapply(cis_lin_clust_list, function(x) max(x$Pcnt_cells))),
unlist(lapply(cis_lin_clust_list, function(x) sum(x$Num_cells))))
df <- data.frame(weighted_mean = sapply(1:length(cis_lin_clust_rand_list), function(y)
weighted.mean(unlist(lapply(cis_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells))),
unlist(lapply(cis_lin_clust_rand_list[[y]], function(x) sum(x$Num_cells))))))
ggplot(df, aes(x = weighted_mean))+geom_histogram(fill = '#C96D29', color = 'black', binwidth = 0.005) + geom_vline(xintercept = wmean, linetype = 'dashed', color = 'red') + geom_text(aes(x = wmean+.03, label = round(wmean, 3), y = 500), color = 'red') + labs(y = 'Number of simulations', x = 'Weighted mean of percent of cells in dominant lineage', title = 'Cis') + xlim(0.15, 0.65) + ylim(0,850) + geom_text(aes(x = wmean+.03, label = 'Observed', y = 525), color = 'red') + theme_classic()
# Cis to DabTram
# Find the weighted mean of the maximum percentage of cells in a single cluster per lineage in each simulation
wmean <- weighted.mean(unlist(lapply(cistodabtram_lin_clust_list, function(x) max(x$Pcnt_cells))),
unlist(lapply(cistodabtram_lin_clust_list, function(x) sum(x$Num_cells))))
df <- data.frame(weighted_mean = sapply(1:length(cistodabtram_lin_clust_rand_list), function(y)
weighted.mean(unlist(lapply(cistodabtram_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells))),
unlist(lapply(cistodabtram_lin_clust_rand_list[[y]], function(x) sum(x$Num_cells))))))
ggplot(df, aes(x = weighted_mean))+geom_histogram(fill = '#A23622', color = 'black', binwidth = 0.005) + geom_vline(xintercept = wmean, linetype = 'dashed', color = 'red') + geom_text(aes(x = wmean+.03, label = round(wmean, 3), y = 500), color = 'red') + labs(y = 'Number of simulations', x = 'Weighted mean of percent of cells in dominant lineage', title = 'Cis to DabTram') + xlim(0.15, 0.65) + ylim(0,850) + geom_text(aes(x = wmean+.03, label = 'Observed', y = 525), color = 'red') + theme_classic()
# Cis to CoCl2
# Find the weighted mean of the maximum percentage of cells in a single cluster per lineage in each simulation
wmean <- weighted.mean(unlist(lapply(cistococl2_lin_clust_list, function(x) max(x$Pcnt_cells))),
unlist(lapply(cistococl2_lin_clust_list, function(x) sum(x$Num_cells))))
df <- data.frame(weighted_mean = sapply(1:length(cistococl2_lin_clust_rand_list), function(y)
weighted.mean(unlist(lapply(cistococl2_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells))),
unlist(lapply(cistococl2_lin_clust_rand_list[[y]], function(x) sum(x$Num_cells))))))
ggplot(df, aes(x = weighted_mean))+geom_histogram(fill = '#C96D29', color = 'black', binwidth = 0.005) + geom_vline(xintercept = wmean, linetype = 'dashed', color = 'red') + geom_text(aes(x = wmean+.03, label = round(wmean, 3), y = 500), color = 'red') + labs(y = 'Number of simulations', x = 'Weighted mean of percent of cells in dominant lineage', title = 'Cis to CoCl2') + xlim(0.15, 0.65) + ylim(0,850) + geom_text(aes(x = wmean+.03, label = 'Observed', y = 525), color = 'red') + theme_classic()
# Cis to Cis
# Find the weighted mean of the maximum percentage of cells in a single cluster per lineage in each simulation
wmean <- weighted.mean(unlist(lapply(cistocis_lin_clust_list, function(x) max(x$Pcnt_cells))),
unlist(lapply(cistocis_lin_clust_list, function(x) sum(x$Num_cells))))
df <- data.frame(weighted_mean = sapply(1:length(cistocis_lin_clust_rand_list), function(y)
weighted.mean(unlist(lapply(cistocis_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells))),
unlist(lapply(cistocis_lin_clust_rand_list[[y]], function(x) sum(x$Num_cells))))))
ggplot(df, aes(x = weighted_mean))+geom_histogram(fill = '#FBD08C', color = 'black', binwidth = 0.005) + geom_vline(xintercept = wmean, linetype = 'dashed', color = 'red') + geom_text(aes(x = wmean+.03, label = round(wmean, 3), y = 500), color = 'red') + labs(y = 'Number of simulations', x = 'Weighted mean of percent of cells in dominant lineage', title = 'Cis to Cis') + xlim(0.15, 0.65) + ylim(0,850) + geom_text(aes(x = wmean+.03, label = 'Observed', y = 525), color = 'red') + theme_classic()
dev.off()
---
title: "Lineage expression over time"
output: html_notebook
---

#Set working directory to appropriate folder for inputs and outputs on Google Drive
```{r, setup, include=FALSE}
#knitr::opts_knit$set(root.dir = '/Volumes/GoogleDrive/My Drive/Fasse_Shared/AJF_Drive_copy/Experiments/AJF009') # for aria's computer
knitr::opts_knit$set(root.dir = '/Volumes/GoogleDrive/.shortcut-targets-by-id/1zSqx3IzXMwt6clUjwyqmlOf4G1K53lvy/Fasse_Shared/AJF_Drive_copy/Experiments/AJF009') # for dylan's computer

#2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/ is additional path for outputs

```

#Initialize
```{r include = FALSE}
rm(list = ls())
library(dplyr)
library(Seurat)
library(ggplot2)
library(RColorBrewer)
library(ggpubr)
library(pheatmap)
library(viridis)

`%nin%` = Negate(`%in%`)
```

# Load data
```{r}
load('2022_01_14_analysis_scripts/2022_05_27_analysis/Assign_dominant_barcodes/all_data_final_lineages.RData')
load('2022_01_14_analysis_scripts/2022_05_27_analysis/Preprocess_GEX/second_timepoint_merged.RData')
load('2022_01_14_analysis_scripts/2022_05_27_analysis/Filtering_cDNA/resistant_lineage_lists.RData')

load('2022_01_14_analysis_scripts/2022_05_27_analysis/Assign_dominant_barcodes/cis_final_lineages.RData')
load('2022_01_14_analysis_scripts/2022_05_27_analysis/Assign_dominant_barcodes/cocl2_final_lineages.RData')
load('2022_01_14_analysis_scripts/2022_05_27_analysis/Assign_dominant_barcodes/dabtram_final_lineages.RData')
load('2022_01_14_analysis_scripts/2022_05_27_analysis/Assign_dominant_barcodes/dabtram_both_times_final_lineages.RData')

```

#Plot cell cycle
```{r}

# view cell cycle scores and phase assignments
DimPlot(all_data, group.by = "Phase")
DimPlot(all_data)
FeaturePlot(object = all_data, reduction = 'umap', features = ("nFeature_RNA"), pt.size = 1, combine = T, order = TRUE)
DimPlot(all_data, group.by = "OG_condition", cols = c('dabtram' = '#623594', 'cocl2' = '#0F8241', 'cis' = '#C96D29', 'dabtramtodabtram' = '#561E59', 'dabtramtococl2' = '#A2248E', 'dabtramtocis' = '#9D85BE', 'cocl2todabtram' = '#10413B', 'cocl2tococl2' = '#6ABD45', 'cocl2tocis' = '#6DC49C', 'cistodabtram' = '#A23622', 'cistococl2' = '#F49129', 'cistocis' = '#FBD08C'))

```

# Markers within each first drug object to find subgroups (ie analogous to NGFR/EGFR)
```{r include = FALSE}
#load('2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/all_data_markers.RData')
cis_markers <- FindAllMarkers(cis, only.pos = TRUE, min.pct = 0.25, logfc.threshold = 0.25)
cocl2_markers <- FindAllMarkers(cocl2, only.pos = TRUE, min.pct = 0.25, logfc.threshold = 0.25)
dabtram_markers <- FindAllMarkers(dabtram, only.pos = TRUE, min.pct = 0.25, logfc.threshold = 0.25)

all_data.markers <- FindAllMarkers(all_data, only.pos = TRUE, min.pct = 0.25, logfc.threshold = 0.25) 
save(all_data.markers, cis_markers, cocl2_markers, dabtram_markers, file = 'all_data_markers.RData')
```

# need to make Idents metadata object which says if the cells are included in the combined lins list, or if they were filtered
```{r}
#find lineages that are maintained at both dabtram timepoints
fivecell_cDNA$DabTramMaintained <- Reduce(intersect, list(fivecell_cDNA$DabTram, fivecell_cDNA$DabTramtoDabTram))

filtered_meta <- rep(0, length(names(all_data$Lineage)))

#specify which cells are in lineages that pass filtering for that condition
filtered_meta[which(all_data$OG_condition == "dabtram" & all_data$Lineage %in% combined_lins_list$DabTram)] <- 'Resistant to DabTram'
filtered_meta[which(all_data$OG_condition == "dabtramtodabtram" & all_data$Lineage %in% combined_lins_list$DabTramtoDabTram)] <- 'Resistant to DabTramtoDabTram'
filtered_meta[which(all_data$OG_condition == "dabtramtococl2" & all_data$Lineage %in% combined_lins_list$DabTramtoCoCl2)] <- 'Resistant to DabTramtoCoCl2'
filtered_meta[which(all_data$OG_condition == "dabtramtocis" & all_data$Lineage %in% combined_lins_list$DabTramtoCis)] <- 'Resistant to DabTramtoCis'
filtered_meta[which(all_data$OG_condition == "cocl2" & all_data$Lineage %in% combined_lins_list$CoCl2)] <- 'Resistant to CoCl2'
filtered_meta[which(all_data$OG_condition == "cocl2todabtram" & all_data$Lineage %in% combined_lins_list$CoCl2toDabTram)] <- 'Resistant to CoCl2toDabTram'
filtered_meta[which(all_data$OG_condition == "cocl2tococl2" & all_data$Lineage %in% combined_lins_list$CoCl2toCoCl2)] <- 'Resistant to CoCl2toCoCl2'
filtered_meta[which(all_data$OG_condition == "cocl2tocis" & all_data$Lineage %in% combined_lins_list$CoCl2toCis)] <- 'Resistant to CoCl2toCis'
filtered_meta[which(all_data$OG_condition == "cis" & all_data$Lineage %in% combined_lins_list$Cis)] <- 'Resistant to Cis'
filtered_meta[which(all_data$OG_condition == "cistodabtram" & all_data$Lineage %in% combined_lins_list$CistoDabTram)] <- 'Resistant to CistoDabTram'
filtered_meta[which(all_data$OG_condition == "cistococl2" & all_data$Lineage %in% combined_lins_list$CistoCoCl2)] <- 'Resistant to CistoCoCl2'
filtered_meta[which(all_data$OG_condition == "cistocis" & all_data$Lineage %in% combined_lins_list$CistoCis)] <- 'Resistant to CistoCis'

#specify which cells are in lineages of more than 5 cells
filtered_meta[which(all_data$OG_condition == "dabtram" & all_data$Lineage %in% fivecell_cDNA$DabTram)] <- 'Large Resistant to DabTram'
filtered_meta[which(all_data$OG_condition == "dabtramtodabtram" & all_data$Lineage %in% fivecell_cDNA$DabTramtoDabTram)] <- 'Large Resistant to DabTramtoDabTram'
filtered_meta[which(all_data$OG_condition == "dabtramtococl2" & all_data$Lineage %in% fivecell_cDNA$DabTramtoCoCl2)] <- 'Large Resistant to DabTramtoCoCl2'
filtered_meta[which(all_data$OG_condition == "dabtramtocis" & all_data$Lineage %in% fivecell_cDNA$DabTramtoCis)] <- 'Large Resistant to DabTramtoCis'
filtered_meta[which(all_data$OG_condition == "cocl2" & all_data$Lineage %in% fivecell_cDNA$CoCl2)] <- 'Large Resistant to CoCl2'
filtered_meta[which(all_data$OG_condition == "cocl2todabtram" & all_data$Lineage %in% fivecell_cDNA$CoCl2toDabTram)] <- 'Large Resistant to CoCl2toDabTram'
filtered_meta[which(all_data$OG_condition == "cocl2tococl2" & all_data$Lineage %in% fivecell_cDNA$CoCl2toCoCl2)] <- 'Large Resistant to CoCl2toCoCl2'
filtered_meta[which(all_data$OG_condition == "cocl2tocis" & all_data$Lineage %in% fivecell_cDNA$CoCl2toCis)] <- 'Large Resistant to CoCl2toCis'
filtered_meta[which(all_data$OG_condition == "cis" & all_data$Lineage %in% fivecell_cDNA$Cis)] <- 'Large Resistant to Cis'
filtered_meta[which(all_data$OG_condition == "cistodabtram" & all_data$Lineage %in% fivecell_cDNA$CistoDabTram)] <- 'Large Resistant to CistoDabTram'
filtered_meta[which(all_data$OG_condition == "cistococl2" & all_data$Lineage %in% fivecell_cDNA$CistoCoCl2)] <- 'Large Resistant to CistoCoCl2'
filtered_meta[which(all_data$OG_condition == "cistocis" & all_data$Lineage %in% fivecell_cDNA$CistoCis)] <- 'Large Resistant to CistoCis'

# filtered_meta[which(all_data$OG_condition == "dabtram" & all_data$Lineage %in% fivecell_cDNA$DabTramMaintained)] <- 'Maintained Resistant to DabTram'
# filtered_meta[which(all_data$OG_condition == "dabtramtodabtram" & all_data$Lineage %in% fivecell_cDNA$DabTramMaintained)] <- 'Maintained Resistant to DabTramtoDabTram'

#specify which cells are in lineages that did not pass filtering
filtered_meta[which(all_data$OG_condition == "dabtram" & all_data$Lineage %nin% combined_lins_list$DabTram & all_data$Lineage %nin% c("No Barcode", "Still multiple"))] <- 'Filtered out' 
filtered_meta[which(all_data$OG_condition == "dabtramtodabtram" & all_data$Lineage %nin% combined_lins_list$DabTramtoDabTram & all_data$Lineage %nin% c("No Barcode", "Still multiple"))] <- 'Filtered out' 
filtered_meta[which(all_data$OG_condition == "dabtramtococl2" & all_data$Lineage %nin% combined_lins_list$DabTramtoCoCl2 & all_data$Lineage %nin% c("No Barcode", "Still multiple"))] <- 'Filtered out' 
filtered_meta[which(all_data$OG_condition == "dabtramtocis" & all_data$Lineage %nin% combined_lins_list$DabTramtoCis & all_data$Lineage %nin% c("No Barcode", "Still multiple"))] <- 'Filtered out' 
filtered_meta[which(all_data$OG_condition == "cocl2" & all_data$Lineage %nin% combined_lins_list$CoCl2 & all_data$Lineage %nin% c("No Barcode", "Still multiple"))] <- 'Filtered out' 
filtered_meta[which(all_data$OG_condition == "cocl2todabtram" & all_data$Lineage %nin% combined_lins_list$CoCl2toDabTram & all_data$Lineage %nin% c("No Barcode", "Still multiple"))] <- 'Filtered out' 
filtered_meta[which(all_data$OG_condition == "cocl2tococl2" & all_data$Lineage %nin% combined_lins_list$CoCl2toCoCl2 & all_data$Lineage %nin% c("No Barcode", "Still multiple"))] <- 'Filtered out' 
filtered_meta[which(all_data$OG_condition == "cocl2tocis" & all_data$Lineage %nin% combined_lins_list$CoCl2toCis & all_data$Lineage %nin% c("No Barcode", "Still multiple"))] <- 'Filtered out' 
filtered_meta[which(all_data$OG_condition == "cis" & all_data$Lineage %nin% combined_lins_list$Cis & all_data$Lineage %nin% c("No Barcode", "Still multiple"))] <- 'Filtered out' 
filtered_meta[which(all_data$OG_condition == "cistodabtram" & all_data$Lineage %nin% combined_lins_list$CistoDabTram & all_data$Lineage %nin% c("No Barcode", "Still multiple"))] <- 'Filtered out' 
filtered_meta[which(all_data$OG_condition == "cistococl2" & all_data$Lineage %nin% combined_lins_list$CistoCoCl2 & all_data$Lineage %nin% c("No Barcode", "Still multiple"))] <- 'Filtered out' 
filtered_meta[which(all_data$OG_condition == "cistocis" & all_data$Lineage %nin% combined_lins_list$CistoCis & all_data$Lineage %nin% c("No Barcode", "Still multiple"))] <- 'Filtered out' 

#specify which cells had zero or multiple barcodes
filtered_meta[which(all_data$Lineage %in% c("No Barcode", "Still multiple"))] <- 'No Barcode'

print(table(filtered_meta))

```

```{r}
all_data$Resistant_filtered <- filtered_meta
Idents(all_data) <- all_data$Resistant_filtered
```

```{r}
pdf('2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/test_plot.pdf', height = 10, width = 20)
DimPlot(all_data, group.by = 'ident', cols = )
DimPlot(all_data, group.by = "Lineage") + theme(legend.position = 'none')
dev.off()

```

```{r}
pdf('2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/test_violin.pdf', height = 10, width = 30)

VlnPlot(all_data, features = 'NGFR', idents = 'Maintained Resistant to DabTram', group.by = "Lineage") + theme(legend.position = 'none') + geom_boxplot(fill = 'white', width = 0.1)
VlnPlot(all_data, features = 'EGFR', idents = 'Maintained Resistant to DabTram', group.by = "Lineage") + theme(legend.position = 'none') + geom_boxplot(fill = 'white', width = 0.1)
VlnPlot(all_data, features = 'NGFR', idents = 'Maintained Resistant to DabTramtoDabTram', group.by = "Lineage") + theme(legend.position = 'none') + geom_boxplot(fill = 'white', width = 0.1)
VlnPlot(all_data, features = 'EGFR', idents = 'Maintained Resistant to DabTramtoDabTram', group.by = "Lineage") + theme(legend.position = 'none') + geom_boxplot(fill = 'white', width = 0.1)

VlnPlot(all_data, features = 'NGFR', idents = 'Large Resistant to DabTram', group.by = "Lineage") + theme(legend.position = 'none') + geom_boxplot(fill = 'white', width = 0.1)
VlnPlot(all_data, features = 'EGFR', idents = 'Large Resistant to DabTram', group.by = "Lineage") + theme(legend.position = 'none') + geom_boxplot(fill = 'white', width = 0.1)
VlnPlot(all_data, features = 'NGFR', idents = 'Large Resistant to DabTramtoDabTram', group.by = "Lineage") + theme(legend.position = 'none') + geom_boxplot(fill = 'white', width = 0.1)
VlnPlot(all_data, features = 'EGFR', idents = 'Large Resistant to DabTramtoDabTram', group.by = "Lineage") + theme(legend.position = 'none') + geom_boxplot(fill = 'white', width = 0.1)

dev.off()
#VlnPlot(all_data, features = 'NGFR', idents = all_data$OG_condition == 'dabtram', group.by = "lineage")
```

#Looking into the one lineage that switches ngfr -> egfr
```{r}

switch_lin_dabtram <- names(all_data$orig.ident[all_data$Lineage == "Lin171516" & all_data$OG_condition == 'dabtram'])
switch_lin_dabtramtodabtram <- names(all_data$orig.ident[all_data$Lineage == "Lin171516" & all_data$OG_condition == 'dabtramtodabtram'])

DimPlot(all_data, group.by = "OG_condition", cols = c('dabtram' = '#623594', 'cocl2' = '#0F8241', 'cis' = '#C96D29', 'dabtramtodabtram' = '#561E59', 'dabtramtococl2' = '#A2248E', 'dabtramtocis' = '#9D85BE', 'cocl2todabtram' = '#10413B', 'cocl2tococl2' = '#6ABD45', 'cocl2tocis' = '#6DC49C', 'cistodabtram' = '#A23622', 'cistococl2' = '#F49129', 'cistocis' = '#FBD08C'))
DimPlot(all_data, cells.highlight = list(switch_lin_dabtram), cols.highlight = c('red'))
DimPlot(all_data, cells.highlight = list(switch_lin_dabtram, switch_lin_dabtramtodabtram), cols.highlight = c('blue', 'red'))

switch_lin <- names(dabtram$orig.ident[dabtram$Lineage == "Lin171516"])
DimPlot(dabtram)
DimPlot(dabtram, cells.highlight = list(switch_lin))

```

#from dabtram_both_times, force 2 clusters in umap, plot vs ngfr egfr, find markers of these 2
```{r}

dabtram_both_times_markers <- FindAllMarkers(dabtram_both_times, only.pos = TRUE, min.pct = 0.25, logfc.threshold = 0.25)
DimPlot(dabtram_both_times)
DimPlot(dabtram_both_times, group.by = 'OG_condition')
FeaturePlot(dabtram_both_times, features = c('NGFR', 'EGFR', 'nFeature_RNA'))
```

#Assign cluster assignments per lineage, find average score per lineage - make plots in order
```{r}
#average cell assignments per lineage in dabtram_maintained

#want to do a stacked barplot here-- copy paste code from condition clustering

#get lineage and cluster data from seurat object, switch cluster identifiers from 0,1 to -1,1 (egfr, ngfr)
clusters_per_lin <- data.frame (Cluster = as.numeric(as.character(dabtram_both_times$seurat_clusters)), Lineage = dabtram_both_times$Lineage, condition = dabtram_both_times$OG_condition)
clusters_per_lin$Cluster[clusters_per_lin$Cluster == 0] <- -1
clusters_per_lin_list <- list()

# Need to get percent values of EGFR for the lineage after the first treatment
for (i in fivecell_cDNA$DabTram){
  currentlin <- filter(clusters_per_lin, clusters_per_lin$Lineage == i & clusters_per_lin$condition == 'dabtram')
  Var1 <- c(-1,1)
  Freq <- c(sum(filter(clusters_per_lin, clusters_per_lin$Lineage == i & clusters_per_lin$condition == 'dabtram')$Cluster == -1), # EGFR
  sum(filter(clusters_per_lin, clusters_per_lin$Lineage == i & clusters_per_lin$condition == 'dabtram')$Cluster == 1))
  clusters_per_lin_list[[i]] <- data.frame('Var1' = Var1, 'Freq' = Freq)
  clusters_per_lin_list[[i]]$Score <- weighted.mean(as.numeric(as.character(clusters_per_lin_list[[i]]$Var1)), clusters_per_lin_list[[i]]$Freq)
}

# Build a dataframe for plotting based on % EGFR or NGFR per lineage after first treatment
clusters_per_lin_df <- data.frame(matrix(ncol = 5, nrow = 0))
colnames(clusters_per_lin_df) <- c('Lineage', 'Percent_cells', 'Num_cells', 'Score')
for (i in names(clusters_per_lin_list)){
    clusters_per_lin_df <- rbind(clusters_per_lin_df, data.frame('Lineage' = i, 'Percent_cells' = clusters_per_lin_list[[i]]$Freq[clusters_per_lin_list[[i]]$Var1 == 1]/sum(clusters_per_lin_list[[i]]$Freq), 'Num_cells' = sum(clusters_per_lin_list[[i]]$Freq[clusters_per_lin_list[[i]]$Var1 == 1]), 'Score' = clusters_per_lin_list[[i]]$Score[1], 'Cluster' = 'NGFR')) # Add NGFR row
  clusters_per_lin_df <- rbind(clusters_per_lin_df, data.frame('Lineage' = i, 'Percent_cells' = clusters_per_lin_list[[i]]$Freq[clusters_per_lin_list[[i]]$Var1 == -1]/sum(clusters_per_lin_list[[i]]$Freq), 'Num_cells' = sum(clusters_per_lin_list[[i]]$Freq[clusters_per_lin_list[[i]]$Var1 == -1]), 'Score' = clusters_per_lin_list[[i]]$Score[1], 'Cluster' = 'EGFR')) # Add EGFR row
}

# Reorder so that EGFR dominant lineages are plot first
clusters_per_lin_df <- clusters_per_lin_df[with(clusters_per_lin_df, order(-Score,Num_cells )),]
clusters_per_lin_df$Lineage <- factor(clusters_per_lin_df$Lineage, levels = rev(unique(clusters_per_lin_df$Lineage)))

# make list object for after second treatment
clusters_per_lin_list_sec <- list()

# Need to get percent values of EGFR for the lineage after the second treatment
for (i in fivecell_cDNA$DabTram){
  currentlin <- filter(clusters_per_lin, clusters_per_lin$Lineage == i & clusters_per_lin$condition == 'dabtramtodabtram')
  Var1 <- c(-1,1)
  Freq <- c(sum(filter(clusters_per_lin, clusters_per_lin$Lineage == i & clusters_per_lin$condition == 'dabtramtodabtram')$Cluster == -1), # EGFR
  sum(filter(clusters_per_lin, clusters_per_lin$Lineage == i & clusters_per_lin$condition == 'dabtramtodabtram')$Cluster == 1))
  clusters_per_lin_list_sec[[i]] <- data.frame('Var1' = Var1, 'Freq' = Freq)
  clusters_per_lin_list_sec[[i]]$Score <- weighted.mean(as.numeric(as.character(clusters_per_lin_list_sec[[i]]$Var1)), clusters_per_lin_list_sec[[i]]$Freq)
}

# Build a dataframe for plotting based on % EGFR or NGFR per lineage after second treatment
clusters_per_lin_df_sec <- data.frame(matrix(ncol = 5, nrow = 0))
colnames(clusters_per_lin_df_sec) <- c('Lineage', 'Percent_cells', 'Num_cells', 'Score', 'Cluster')
for (i in names(clusters_per_lin_list)){

  if (sum(clusters_per_lin_list_sec[[i]]$Freq) < 5){
    clusters_per_lin_df_sec <- rbind(clusters_per_lin_df_sec, data.frame('Lineage' = i, 'Percent_cells' = 1, 'Num_cells' = 0, 'Score' = 0, 'Cluster' = 'Died')) # Add Lineage died row
    clusters_per_lin_df_sec <- rbind(clusters_per_lin_df_sec, data.frame('Lineage' = i, 'Percent_cells' = 0, 'Num_cells' = 0, 'Score' = 0, 'Cluster' = 'NGFR')) # Add NGFR row
    clusters_per_lin_df_sec <- rbind(clusters_per_lin_df_sec, data.frame('Lineage' = i, 'Percent_cells' = 0, 'Num_cells' = 0, 'Score' = 0, 'Cluster' = 'EGFR')) # Add EGFR row
  }else{ # Actually calculate percentages since the lineage survived/is more than 5 cells
      clusters_per_lin_df_sec <- rbind(clusters_per_lin_df_sec, data.frame('Lineage' = i, 'Percent_cells' = 0, 'Num_cells' = 0, 'Score' = clusters_per_lin_list_sec[[i]]$Score[1], 'Cluster' = 'Died')) # Add Lineage died row
      clusters_per_lin_df_sec <- rbind(clusters_per_lin_df_sec, data.frame('Lineage' = i, 'Percent_cells' = clusters_per_lin_list_sec[[i]]$Freq[clusters_per_lin_list_sec[[i]]$Var1 == 1]/sum(clusters_per_lin_list_sec[[i]]$Freq), 'Num_cells' = sum(clusters_per_lin_list_sec[[i]]$Freq[clusters_per_lin_list_sec[[i]]$Var1 == 1]), 'Score' = clusters_per_lin_list_sec[[i]]$Score[1], 'Cluster' = 'NGFR')) # Add NGFR row
      clusters_per_lin_df_sec <- rbind(clusters_per_lin_df_sec, data.frame('Lineage' = i, 'Percent_cells' = clusters_per_lin_list_sec[[i]]$Freq[clusters_per_lin_list_sec[[i]]$Var1 == -1]/sum(clusters_per_lin_list_sec[[i]]$Freq), 'Num_cells' = sum(clusters_per_lin_list_sec[[i]]$Freq[clusters_per_lin_list_sec[[i]]$Var1 == -1]), 'Score' = clusters_per_lin_list_sec[[i]]$Score[1], 'Cluster' = 'EGFR')) # Add EGFR row
    }
}

# Reorder so that EGFR dominant lineages are plot first
clusters_per_lin_df_sec$Lineage <- factor(clusters_per_lin_df_sec$Lineage, levels = rev(unique(clusters_per_lin_df$Lineage)))

# Plot
p1 <- ggplot(clusters_per_lin_df, aes(y = Percent_cells, x = Lineage, fill = Cluster)) + geom_bar(stat = 'identity', position = position_fill(reverse = T), color = '#D3D3D3') + scale_fill_manual(values = c("#000000","#FFFFFF")) + theme_classic() + theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))
p2 <- ggplot(clusters_per_lin_df_sec, aes(y = Percent_cells, x = Lineage, fill = Cluster)) + geom_bar(stat = 'identity', position = position_fill(reverse = T), color = '#D3D3D3') + scale_fill_manual(values = c("#FF0000","#000000", '#FFFFFF')) + theme_classic() + theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))

pdf('2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/stacked_bar_EGFR_NGFR_Died.pdf')
ggarrange(p1,p2, nrow = 2)
dev.off()
```

# Plot the number of cells per lineage in second drug for lineages that survived in DabTram
```{r}
# Get the number of cells in the DabTram resistant lineages in the conditions that got DabTram first
dabtramtococl2_dt_resist_lins <- list()
dabtramtocis_dt_resist_lins <- list()

for (i in fivecell_cDNA$DabTram){
  dabtramtococl2_dt_resist_lins[[i]] <- all_data$Lineage[all_data$Lineage == i & all_data$OG_condition == 'dabtramtococl2']
  dabtramtocis_dt_resist_lins[[i]] <- all_data$Lineage[all_data$Lineage == i & all_data$OG_condition == 'dabtramtocis']
}

# convert list to dataframe
dabtramtococl2_dt_resist_lins_df <- data.frame(num_cells = lengths(dabtramtococl2_dt_resist_lins))
dabtramtococl2_dt_resist_lins_df$Lineage = rownames(dabtramtococl2_dt_resist_lins_df)
dabtramtococl2_dt_resist_lins_df$Lineage <- factor(dabtramtococl2_dt_resist_lins_df$Lineage, levels = rev(unique(clusters_per_lin_df$Lineage)))

dabtramtocis_dt_resist_lins_df <- data.frame(num_cells = lengths(dabtramtocis_dt_resist_lins))
dabtramtocis_dt_resist_lins_df$Lineage = rownames(dabtramtocis_dt_resist_lins_df)
dabtramtocis_dt_resist_lins_df$Lineage <- factor(dabtramtocis_dt_resist_lins_df$Lineage, levels = rev(unique(clusters_per_lin_df$Lineage)))

# Plot 
p3 <- ggplot(dabtramtococl2_dt_resist_lins_df, aes(y = log(num_cells+1), x = Lineage)) + geom_col(fill = '#A2248E') + theme_classic() + theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1)) + labs(title = 'DabTram to CoCl2')

p4 <- ggplot(dabtramtocis_dt_resist_lins_df, aes(y = log(num_cells+1), x = Lineage)) + geom_col(fill = '#9D85BE') + theme_classic() + theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1)) + labs(title = 'DabTram to Cis') 

pdf('2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/stacked_bar_EGFR_NGFR_Died_w_other_second_drugs.pdf', height = 14)
ggarrange(p1+ theme(legend.position = 'bottom'),p2+ theme(legend.position = 'bottom'),p3,p4, nrow = 4)
dev.off()

# Also look at reads in gDNA of these plots?????
```

# Look at whether lineages cluster together in each individual condition - Starting with DabTram
```{r}
rm(dabtram, cis, cocl2) # Remove old seurat objects that are no longer needed
Idents(all_data) <- all_data$OG_condition # Change the idents to the OG condition for subsetting to dabtram
dabtram <- subset(all_data, idents = 'dabtram') # Subset down to the dabtram object

ElbowPlot(dabtram) # The standard deviation seems to really level off at 10

# Recluster with the appropriate number of dimensions
dabtram <- FindNeighbors(dabtram, dims = 1:10)
dabtram <- FindClusters(dabtram, resolution = 0.5)
dabtram <- RunUMAP(dabtram, dims = 1:10)
DimPlot(dabtram, reduction = 'umap')

# Build list of lineages with at least 5 cells in dab tram and check what seurat cluster the cells are in 
dabtram_lin_clust_list <- list()

for (i in fivecell_cDNA$DabTram){
  temp_df <- as.data.frame(table(Idents(dabtram)[dabtram$Lineage == i]))
  colnames(temp_df) <- c('Seurat_clust', 'Num_cells')
  temp_df$Pcnt_cells <- temp_df$Num_cells/sum(temp_df$Num_cells)
  dabtram_lin_clust_list[[i]] <- temp_df
}

# Need to do a random sampling of the same thing 
dabtram_lin_clust_rand_list <- list()
num_iter <- 800 
for(j in 1:num_iter){
  set.seed(j)
  dabtram_lin_clust_rand_list[[j]] <- list()
  for (i in fivecell_cDNA$DabTram){
    num_cells <- length(dabtram$Lineage[dabtram$Lineage == i])
    temp_df <- as.data.frame(table(sample(Idents(dabtram), num_cells, replace = F)))
    colnames(temp_df) <- c('Seurat_clust', 'Num_cells')
    temp_df$Pcnt_cells <- temp_df$Num_cells/sum(temp_df$Num_cells)
    dabtram_lin_clust_rand_list[[j]][[i]] <- temp_df
  }
}
```
# Significance testing of the dabtram simulation
```{r}
# Find the mean of the maximum percentage of cells in a single cluster per lineage in each simulation
mean_dabtram <- mean(unlist(lapply(dabtram_lin_clust_list, function(x) max(x$Pcnt_cells))))
means_dabtram_sim <- sapply(1:length(dabtram_lin_clust_rand_list), function(y)
  mean(unlist(lapply(dabtram_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells)))))
z_mean_dabtram <- (mean_dabtram-mean(means_dabtram_sim))/sd(means_dabtram_sim)
pval_mean_dabtram <- pnorm(z_mean_dabtram, mean(means_dabtram_sim), sd(means_dabtram_sim), lower.tail = F)

# Find the weighted mean of the maximum percentage of cells in a single cluster per lineage in each simulation
weighted_mean_dabtram <- weighted.mean(unlist(lapply(dabtram_lin_clust_list, function(x) max(x$Pcnt_cells))),
unlist(lapply(dabtram_lin_clust_list, function(x) sum(x$Num_cells))))
weighted_means_dabtram_sim <- sapply(1:length(dabtram_lin_clust_rand_list), function(y)
  weighted.mean(unlist(lapply(dabtram_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells))),
                unlist(lapply(dabtram_lin_clust_rand_list[[y]], function(x) sum(x$Num_cells)))))
z_wmean_dabtram <- (weighted_mean_dabtram-mean(weighted_means_dabtram_sim))/sd(weighted_means_dabtram_sim)
pval_wmean_dabtram <- pnorm(z_wmean_dabtram, mean(weighted_means_dabtram_sim), sd(weighted_means_dabtram_sim), lower.tail = F)

# Compare each individual distribution of maxes to the observed max by t test and track pval
ttest_pval_dabtram <- c()
for (i in 1:length(means_dabtram_sim)){
  sim_maxes <- unlist(lapply(dabtram_lin_clust_rand_list[[i]], function(x) max(x$Pcnt_cells)))
  ttest_pval_dabtram <- cbind(ttest_pval_dabtram,t.test(x = unlist(lapply(dabtram_lin_clust_list, function(x) max(x$Pcnt_cells))),y = unlist(lapply(dabtram_lin_clust_rand_list[[i]], function(x) max(x$Pcnt_cells))), alternative = 'greater')$p.value)
}

# Save outputs
save(dabtram_lin_clust_list, dabtram_lin_clust_rand_list,z_mean_dabtram,pval_mean_dabtram, z_wmean_dabtram, ttest_pval_dabtram, file = '2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/dabtram_sim_results.RData')
rm(dabtram)
```

# Look at whether lineages cluster together in each individual condition -  DabTramtoDabTram
```{r}
Idents(all_data) <- all_data$OG_condition # Change the idents to the OG condition for subsetting to dabtramtodabtram
dabtramtodabtram <- subset(all_data, idents = 'dabtramtodabtram') # Subset down to the dabtramtodabtram object

ElbowPlot(dabtramtodabtram) # The standard deviation seems to really level off at 10

# Recluster with the appropriate number of dimensions
dabtramtodabtram <- FindNeighbors(dabtramtodabtram, dims = 1:10)
dabtramtodabtram <- FindClusters(dabtramtodabtram, resolution = 0.5)
dabtramtodabtram <- RunUMAP(dabtramtodabtram, dims = 1:10)
DimPlot(dabtramtodabtram, reduction = 'umap')

# Build list of lineages with at least 5 cells in dab tram and check what seurat cluster the cells are in 
dabtramtodabtram_lin_clust_list <- list()

for (i in fivecell_cDNA$DabTramtoDabTram){
  temp_df <- as.data.frame(table(Idents(dabtramtodabtram)[dabtramtodabtram$Lineage == i]))
  colnames(temp_df) <- c('Seurat_clust', 'Num_cells')
  temp_df$Pcnt_cells <- temp_df$Num_cells/sum(temp_df$Num_cells)
  dabtramtodabtram_lin_clust_list[[i]] <- temp_df
}

# Need to do a random sampling of the same thing 
dabtramtodabtram_lin_clust_rand_list <- list()
num_iter <- 800 
for(j in 1:num_iter){
  set.seed(j)
  dabtramtodabtram_lin_clust_rand_list[[j]] <- list()
  for (i in fivecell_cDNA$DabTramtoDabTram){
    num_cells <- length(dabtramtodabtram$Lineage[dabtramtodabtram$Lineage == i])
    temp_df <- as.data.frame(table(sample(Idents(dabtramtodabtram), num_cells, replace = F)))
    colnames(temp_df) <- c('Seurat_clust', 'Num_cells')
    temp_df$Pcnt_cells <- temp_df$Num_cells/sum(temp_df$Num_cells)
    dabtramtodabtram_lin_clust_rand_list[[j]][[i]] <- temp_df
  }
}
```

# Significance testing of the dabtramtodabtram simulation
```{r}
# Find the mean of the maximum percentage of cells in a single cluster per lineage in each simulation
mean_dabtramtodabtram <- mean(unlist(lapply(dabtramtodabtram_lin_clust_list, function(x) max(x$Pcnt_cells))))
means_dabtramtodabtram_sim <- sapply(1:length(dabtramtodabtram_lin_clust_rand_list), function(y)
  mean(unlist(lapply(dabtramtodabtram_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells)))))
z_mean_dabtramtodabtram <- (mean_dabtramtodabtram-mean(means_dabtramtodabtram_sim))/sd(means_dabtramtodabtram_sim)
pval_mean_dabtramtodabtram <- pnorm(z_mean_dabtramtodabtram, mean(means_dabtramtodabtram_sim), sd(means_dabtramtodabtram_sim), lower.tail = F)

# Find the weighted mean of the maximum percentage of cells in a single cluster per lineage in each simulation
weighted_mean_dabtramtodabtram <- weighted.mean(unlist(lapply(dabtramtodabtram_lin_clust_list, function(x) max(x$Pcnt_cells))),
unlist(lapply(dabtramtodabtram_lin_clust_list, function(x) sum(x$Num_cells))))
weighted_means_dabtramtodabtram_sim <- sapply(1:length(dabtramtodabtram_lin_clust_rand_list), function(y)
  weighted.mean(unlist(lapply(dabtramtodabtram_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells))),
                unlist(lapply(dabtramtodabtram_lin_clust_rand_list[[y]], function(x) sum(x$Num_cells)))))
z_wmean_dabtramtodabtram <- (weighted_mean_dabtramtodabtram-mean(weighted_means_dabtramtodabtram_sim))/sd(weighted_means_dabtramtodabtram_sim)
pval_wmean_dabtramtodabtram <- pnorm(z_wmean_dabtramtodabtram, mean(weighted_means_dabtramtodabtram_sim), sd(weighted_means_dabtramtodabtram_sim), lower.tail = F)

# Compare each individual distribution of maxes to the observed max by t test and track pval
ttest_pval_dabtramtodabtram <- c()
for (i in 1:length(means_dabtramtodabtram_sim)){
  sim_maxes <- unlist(lapply(dabtramtodabtram_lin_clust_rand_list[[i]], function(x) max(x$Pcnt_cells)))
  ttest_pval_dabtramtodabtram <- cbind(ttest_pval_dabtramtodabtram,t.test(x = unlist(lapply(dabtramtodabtram_lin_clust_list, function(x) max(x$Pcnt_cells))),y = unlist(lapply(dabtramtodabtram_lin_clust_rand_list[[i]], function(x) max(x$Pcnt_cells))), alternative = 'greater')$p.value)
}

# Save outputs
save(dabtramtodabtram_lin_clust_list, dabtramtodabtram_lin_clust_rand_list,z_mean_dabtramtodabtram,pval_mean_dabtramtodabtram, z_wmean_dabtramtodabtram, ttest_pval_dabtramtodabtram, file = '2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/dabtramtodabtram_sim_results.RData')
rm(dabtramtodabtram)
```

# Look at whether lineages cluster together in each individual condition -  DabTramtoCis
```{r}
Idents(all_data) <- all_data$OG_condition # Change the idents to the OG condition for subsetting to dabtramtocis
dabtramtocis <- subset(all_data, idents = 'dabtramtocis') # Subset down to the dabtramtocis object

ElbowPlot(dabtramtocis) # The standard deviation seems to really level off at 10

# Recluster with the appropriate number of dimensions
dabtramtocis <- FindNeighbors(dabtramtocis, dims = 1:10)
dabtramtocis <- FindClusters(dabtramtocis, resolution = 0.5)
dabtramtocis <- RunUMAP(dabtramtocis, dims = 1:10)
DimPlot(dabtramtocis, reduction = 'umap')

# Build list of lineages with at least 5 cells in dab tram and check what seurat cluster the cells are in 
dabtramtocis_lin_clust_list <- list()

for (i in fivecell_cDNA$DabTramtoCis){
  temp_df <- as.data.frame(table(Idents(dabtramtocis)[dabtramtocis$Lineage == i]))
  colnames(temp_df) <- c('Seurat_clust', 'Num_cells')
  temp_df$Pcnt_cells <- temp_df$Num_cells/sum(temp_df$Num_cells)
  dabtramtocis_lin_clust_list[[i]] <- temp_df
}

# Need to do a random sampling of the same thing 
dabtramtocis_lin_clust_rand_list <- list()
num_iter <- 800 
for(j in 1:num_iter){
  set.seed(j)
  dabtramtocis_lin_clust_rand_list[[j]] <- list()
  for (i in fivecell_cDNA$DabTramtoCis){
    num_cells <- length(dabtramtocis$Lineage[dabtramtocis$Lineage == i])
    temp_df <- as.data.frame(table(sample(Idents(dabtramtocis), num_cells, replace = F)))
    colnames(temp_df) <- c('Seurat_clust', 'Num_cells')
    temp_df$Pcnt_cells <- temp_df$Num_cells/sum(temp_df$Num_cells)
    dabtramtocis_lin_clust_rand_list[[j]][[i]] <- temp_df
  }
}
```

# Significance testing of the dabtramtocis simulation
```{r}
# Find the mean of the maximum percentage of cells in a single cluster per lineage in each simulation
mean_dabtramtocis <- mean(unlist(lapply(dabtramtocis_lin_clust_list, function(x) max(x$Pcnt_cells))))
means_dabtramtocis_sim <- sapply(1:length(dabtramtocis_lin_clust_rand_list), function(y)
  mean(unlist(lapply(dabtramtocis_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells)))))
z_mean_dabtramtocis <- (mean_dabtramtocis-mean(means_dabtramtocis_sim))/sd(means_dabtramtocis_sim)
pval_mean_dabtramtocis <- pnorm(z_mean_dabtramtocis, mean(means_dabtramtocis_sim), sd(means_dabtramtocis_sim), lower.tail = F)

# Find the weighted mean of the maximum percentage of cells in a single cluster per lineage in each simulation
weighted_mean_dabtramtocis <- weighted.mean(unlist(lapply(dabtramtocis_lin_clust_list, function(x) max(x$Pcnt_cells))),
unlist(lapply(dabtramtocis_lin_clust_list, function(x) sum(x$Num_cells))))
weighted_means_dabtramtocis_sim <- sapply(1:length(dabtramtocis_lin_clust_rand_list), function(y)
  weighted.mean(unlist(lapply(dabtramtocis_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells))),
                unlist(lapply(dabtramtocis_lin_clust_rand_list[[y]], function(x) sum(x$Num_cells)))))
z_wmean_dabtramtocis <- (weighted_mean_dabtramtocis-mean(weighted_means_dabtramtocis_sim))/sd(weighted_means_dabtramtocis_sim)
pval_wmean_dabtramtocis <- pnorm(z_wmean_dabtramtocis, mean(weighted_means_dabtramtocis_sim), sd(weighted_means_dabtramtocis_sim), lower.tail = F)

# Compare each individual distribution of maxes to the observed max by t test and track pval
ttest_pval_dabtramtocis <- c()
for (i in 1:length(means_dabtramtocis_sim)){
  sim_maxes <- unlist(lapply(dabtramtocis_lin_clust_rand_list[[i]], function(x) max(x$Pcnt_cells)))
  ttest_pval_dabtramtocis <- cbind(ttest_pval_dabtramtocis,t.test(x = unlist(lapply(dabtramtocis_lin_clust_list, function(x) max(x$Pcnt_cells))),y = unlist(lapply(dabtramtocis_lin_clust_rand_list[[i]], function(x) max(x$Pcnt_cells))), alternative = 'greater')$p.value)
}

# Save outputs
save(dabtramtocis_lin_clust_list, dabtramtocis_lin_clust_rand_list,z_mean_dabtramtocis,pval_mean_dabtramtocis, z_wmean_dabtramtocis, ttest_pval_dabtramtocis, file = '2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/dabtramtocis_sim_results.RData')
rm(dabtramtocis)
```

# Look at whether lineages cluster together in each individual condition -  DabTramtoCis
```{r}
Idents(all_data) <- all_data$OG_condition # Change the idents to the OG condition for subsetting to dabtramtococl2
dabtramtococl2 <- subset(all_data, idents = 'dabtramtococl2') # Subset down to the dabtramtococl2 object

ElbowPlot(dabtramtococl2) # The standard deviation seems to really level off at 10

# Recluster with the appropriate number of dimensions
dabtramtococl2 <- FindNeighbors(dabtramtococl2, dims = 1:10)
dabtramtococl2 <- FindClusters(dabtramtococl2, resolution = 0.5)
dabtramtococl2 <- RunUMAP(dabtramtococl2, dims = 1:10)
DimPlot(dabtramtococl2, reduction = 'umap')

# Build list of lineages with at least 5 cells in dab tram and check what seurat cluster the cells are in 
dabtramtococl2_lin_clust_list <- list()

for (i in fivecell_cDNA$DabTramtoCoCl2){
  temp_df <- as.data.frame(table(Idents(dabtramtococl2)[dabtramtococl2$Lineage == i]))
  colnames(temp_df) <- c('Seurat_clust', 'Num_cells')
  temp_df$Pcnt_cells <- temp_df$Num_cells/sum(temp_df$Num_cells)
  dabtramtococl2_lin_clust_list[[i]] <- temp_df
}

# Need to do a random sampling of the same thing 
dabtramtococl2_lin_clust_rand_list <- list()
num_iter <- 800 
for(j in 1:num_iter){
  set.seed(j)
  dabtramtococl2_lin_clust_rand_list[[j]] <- list()
  for (i in fivecell_cDNA$DabTramtoCoCl2){
    num_cells <- length(dabtramtococl2$Lineage[dabtramtococl2$Lineage == i])
    temp_df <- as.data.frame(table(sample(Idents(dabtramtococl2), num_cells, replace = F)))
    colnames(temp_df) <- c('Seurat_clust', 'Num_cells')
    temp_df$Pcnt_cells <- temp_df$Num_cells/sum(temp_df$Num_cells)
    dabtramtococl2_lin_clust_rand_list[[j]][[i]] <- temp_df
  }
}
```

# Significance testing of the dabtramtococl2 simulation
```{r}
# Find the mean of the maximum percentage of cells in a single cluster per lineage in each simulation
mean_dabtramtococl2 <- mean(unlist(lapply(dabtramtococl2_lin_clust_list, function(x) max(x$Pcnt_cells))))
means_dabtramtococl2_sim <- sapply(1:length(dabtramtococl2_lin_clust_rand_list), function(y)
  mean(unlist(lapply(dabtramtococl2_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells)))))
z_mean_dabtramtococl2 <- (mean_dabtramtococl2-mean(means_dabtramtococl2_sim))/sd(means_dabtramtococl2_sim)
pval_mean_dabtramtococl2 <- pnorm(z_mean_dabtramtococl2, mean(means_dabtramtococl2_sim), sd(means_dabtramtococl2_sim), lower.tail = F)

# Find the weighted mean of the maximum percentage of cells in a single cluster per lineage in each simulation
weighted_mean_dabtramtococl2 <- weighted.mean(unlist(lapply(dabtramtococl2_lin_clust_list, function(x) max(x$Pcnt_cells))),
unlist(lapply(dabtramtococl2_lin_clust_list, function(x) sum(x$Num_cells))))
weighted_means_dabtramtococl2_sim <- sapply(1:length(dabtramtococl2_lin_clust_rand_list), function(y)
  weighted.mean(unlist(lapply(dabtramtococl2_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells))),
                unlist(lapply(dabtramtococl2_lin_clust_rand_list[[y]], function(x) sum(x$Num_cells)))))
z_wmean_dabtramtococl2 <- (weighted_mean_dabtramtococl2-mean(weighted_means_dabtramtococl2_sim))/sd(weighted_means_dabtramtococl2_sim)
pval_wmean_dabtramtococl2 <- pnorm(z_wmean_dabtramtococl2, mean(weighted_means_dabtramtococl2_sim), sd(weighted_means_dabtramtococl2_sim), lower.tail = F)

# Compare each individual distribution of maxes to the observed max by t test and track pval
ttest_pval_dabtramtococl2 <- c()
for (i in 1:length(means_dabtramtococl2_sim)){
  sim_maxes <- unlist(lapply(dabtramtococl2_lin_clust_rand_list[[i]], function(x) max(x$Pcnt_cells)))
  ttest_pval_dabtramtococl2 <- cbind(ttest_pval_dabtramtococl2,t.test(x = unlist(lapply(dabtramtococl2_lin_clust_list, function(x) max(x$Pcnt_cells))),y = unlist(lapply(dabtramtococl2_lin_clust_rand_list[[i]], function(x) max(x$Pcnt_cells))), alternative = 'greater')$p.value)
}

# Save outputs
save(dabtramtococl2_lin_clust_list, dabtramtococl2_lin_clust_rand_list,z_mean_dabtramtococl2,pval_mean_dabtramtococl2, z_wmean_dabtramtococl2, ttest_pval_dabtramtococl2, file = '2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/dabtramtococl2_sim_results.RData')
rm(dabtramtococl2)
```

# Look at whether lineages cluster together in each individual condition - Starting with DabTram
```{r}
Idents(all_data) <- all_data$OG_condition # Change the idents to the OG condition for subsetting to cocl2
cocl2 <- subset(all_data, idents = 'cocl2') # Subset down to the cocl2 object

ElbowPlot(cocl2) # The standard deviation seems to really level off at 10

# Recluster with the appropriate number of dimensions
cocl2 <- FindNeighbors(cocl2, dims = 1:10)
cocl2 <- FindClusters(cocl2, resolution = 0.5)
cocl2 <- RunUMAP(cocl2, dims = 1:10)
DimPlot(cocl2, reduction = 'umap')

# Build list of lineages with at least 5 cells in dab tram and check what seurat cluster the cells are in 
cocl2_lin_clust_list <- list()

for (i in fivecell_cDNA$CoCl2){
  temp_df <- as.data.frame(table(Idents(cocl2)[cocl2$Lineage == i]))
  colnames(temp_df) <- c('Seurat_clust', 'Num_cells')
  temp_df$Pcnt_cells <- temp_df$Num_cells/sum(temp_df$Num_cells)
  cocl2_lin_clust_list[[i]] <- temp_df
}

# Need to do a random sampling of the same thing 
cocl2_lin_clust_rand_list <- list()
num_iter <- 800 
for(j in 1:num_iter){
  set.seed(j)
  cocl2_lin_clust_rand_list[[j]] <- list()
  for (i in fivecell_cDNA$CoCl2){
    num_cells <- length(cocl2$Lineage[cocl2$Lineage == i])
    temp_df <- as.data.frame(table(sample(Idents(cocl2), num_cells, replace = F)))
    colnames(temp_df) <- c('Seurat_clust', 'Num_cells')
    temp_df$Pcnt_cells <- temp_df$Num_cells/sum(temp_df$Num_cells)
    cocl2_lin_clust_rand_list[[j]][[i]] <- temp_df
  }
}
```
# Significance testing of the cocl2 simulation
```{r}
# Find the mean of the maximum percentage of cells in a single cluster per lineage in each simulation
mean_cocl2 <- mean(unlist(lapply(cocl2_lin_clust_list, function(x) max(x$Pcnt_cells))))
means_cocl2_sim <- sapply(1:length(cocl2_lin_clust_rand_list), function(y)
  mean(unlist(lapply(cocl2_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells)))))
z_mean_cocl2 <- (mean_cocl2-mean(means_cocl2_sim))/sd(means_cocl2_sim)
pval_mean_cocl2 <- pnorm(z_mean_cocl2, mean(means_cocl2_sim), sd(means_cocl2_sim), lower.tail = F)

# Find the weighted mean of the maximum percentage of cells in a single cluster per lineage in each simulation
weighted_mean_cocl2 <- weighted.mean(unlist(lapply(cocl2_lin_clust_list, function(x) max(x$Pcnt_cells))),
unlist(lapply(cocl2_lin_clust_list, function(x) sum(x$Num_cells))))
weighted_means_cocl2_sim <- sapply(1:length(cocl2_lin_clust_rand_list), function(y)
  weighted.mean(unlist(lapply(cocl2_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells))),
                unlist(lapply(cocl2_lin_clust_rand_list[[y]], function(x) sum(x$Num_cells)))))
z_wmean_cocl2 <- (weighted_mean_cocl2-mean(weighted_means_cocl2_sim))/sd(weighted_means_cocl2_sim)
pval_wmean_cocl2 <- pnorm(z_wmean_cocl2, mean(weighted_means_cocl2_sim), sd(weighted_means_cocl2_sim), lower.tail = F)

# Compare each individual distribution of maxes to the observed max by t test and track pval
ttest_pval_cocl2 <- c()
for (i in 1:length(means_cocl2_sim)){
  sim_maxes <- unlist(lapply(cocl2_lin_clust_rand_list[[i]], function(x) max(x$Pcnt_cells)))
  ttest_pval_cocl2 <- cbind(ttest_pval_cocl2,t.test(x = unlist(lapply(cocl2_lin_clust_list, function(x) max(x$Pcnt_cells))),y = unlist(lapply(cocl2_lin_clust_rand_list[[i]], function(x) max(x$Pcnt_cells))), alternative = 'greater')$p.value)
}

# Save outputs
save(cocl2_lin_clust_list, cocl2_lin_clust_rand_list,z_mean_cocl2,pval_mean_cocl2, z_wmean_cocl2, ttest_pval_cocl2, file = '2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/cocl2_sim_results.RData')
rm(cocl2)
```

# Look at whether lineages cluster together in each individual condition - Starting with CoCl2toCoCl2
```{r}
Idents(all_data) <- all_data$OG_condition # Change the idents to the OG condition for subsetting to cocl2tococl2
cocl2tococl2 <- subset(all_data, idents = 'cocl2tococl2') # Subset down to the cocl2tococl2 object

ElbowPlot(cocl2tococl2) # The standard deviation seems to really level off at 10

# Recluster with the appropriate number of dimensions
cocl2tococl2 <- FindNeighbors(cocl2tococl2, dims = 1:10)
cocl2tococl2 <- FindClusters(cocl2tococl2, resolution = 0.5)
cocl2tococl2 <- RunUMAP(cocl2tococl2, dims = 1:10)
DimPlot(cocl2tococl2, reduction = 'umap')

# Build list of lineages with at least 5 cells in dab tram and check what seurat cluster the cells are in 
cocl2tococl2_lin_clust_list <- list()

for (i in fivecell_cDNA$CoCl2toCoCl2){
  temp_df <- as.data.frame(table(Idents(cocl2tococl2)[cocl2tococl2$Lineage == i]))
  colnames(temp_df) <- c('Seurat_clust', 'Num_cells')
  temp_df$Pcnt_cells <- temp_df$Num_cells/sum(temp_df$Num_cells)
  cocl2tococl2_lin_clust_list[[i]] <- temp_df
}

# Need to do a random sampling of the same thing 
cocl2tococl2_lin_clust_rand_list <- list()
num_iter <- 800 
for(j in 1:num_iter){
  set.seed(j)
  cocl2tococl2_lin_clust_rand_list[[j]] <- list()
  for (i in fivecell_cDNA$CoCl2toCoCl2){
    num_cells <- length(cocl2tococl2$Lineage[cocl2tococl2$Lineage == i])
    temp_df <- as.data.frame(table(sample(Idents(cocl2tococl2), num_cells, replace = F)))
    colnames(temp_df) <- c('Seurat_clust', 'Num_cells')
    temp_df$Pcnt_cells <- temp_df$Num_cells/sum(temp_df$Num_cells)
    cocl2tococl2_lin_clust_rand_list[[j]][[i]] <- temp_df
  }
}
```
# Significance testing of the cocl2tococl2 simulation
```{r}
# Find the mean of the maximum percentage of cells in a single cluster per lineage in each simulation
mean_cocl2tococl2 <- mean(unlist(lapply(cocl2tococl2_lin_clust_list, function(x) max(x$Pcnt_cells))))
means_cocl2tococl2_sim <- sapply(1:length(cocl2tococl2_lin_clust_rand_list), function(y)
  mean(unlist(lapply(cocl2tococl2_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells)))))
z_mean_cocl2tococl2 <- (mean_cocl2tococl2-mean(means_cocl2tococl2_sim))/sd(means_cocl2tococl2_sim)
pval_mean_cocl2tococl2 <- pnorm(z_mean_cocl2tococl2, mean(means_cocl2tococl2_sim), sd(means_cocl2tococl2_sim), lower.tail = F)

# Find the weighted mean of the maximum percentage of cells in a single cluster per lineage in each simulation
weighted_mean_cocl2tococl2 <- weighted.mean(unlist(lapply(cocl2tococl2_lin_clust_list, function(x) max(x$Pcnt_cells))),
unlist(lapply(cocl2tococl2_lin_clust_list, function(x) sum(x$Num_cells))))
weighted_means_cocl2tococl2_sim <- sapply(1:length(cocl2tococl2_lin_clust_rand_list), function(y)
  weighted.mean(unlist(lapply(cocl2tococl2_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells))),
                unlist(lapply(cocl2tococl2_lin_clust_rand_list[[y]], function(x) sum(x$Num_cells)))))
z_wmean_cocl2tococl2 <- (weighted_mean_cocl2tococl2-mean(weighted_means_cocl2tococl2_sim))/sd(weighted_means_cocl2tococl2_sim)
pval_wmean_cocl2tococl2 <- pnorm(z_wmean_cocl2tococl2, mean(weighted_means_cocl2tococl2_sim), sd(weighted_means_cocl2tococl2_sim), lower.tail = F)

# Compare each individual distribution of maxes to the observed max by t test and track pval
ttest_pval_cocl2tococl2 <- c()
for (i in 1:length(means_cocl2tococl2_sim)){
  sim_maxes <- unlist(lapply(cocl2tococl2_lin_clust_rand_list[[i]], function(x) max(x$Pcnt_cells)))
  ttest_pval_cocl2tococl2 <- cbind(ttest_pval_cocl2tococl2,t.test(x = unlist(lapply(cocl2tococl2_lin_clust_list, function(x) max(x$Pcnt_cells))),y = unlist(lapply(cocl2tococl2_lin_clust_rand_list[[i]], function(x) max(x$Pcnt_cells))), alternative = 'greater')$p.value)
}

# Save outputs
save(cocl2tococl2_lin_clust_list, cocl2tococl2_lin_clust_rand_list,z_mean_cocl2tococl2,pval_mean_cocl2tococl2, z_wmean_cocl2tococl2, ttest_pval_cocl2tococl2, file = '2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/cocl2tococl2_sim_results.RData')
rm(cocl2tococl2)
```

# Look at whether lineages cluster together in each individual condition - Starting with CoCl2toCis
```{r}
Idents(all_data) <- all_data$OG_condition # Change the idents to the OG condition for subsetting to cocl2tocis
cocl2tocis <- subset(all_data, idents = 'cocl2tocis') # Subset down to the cocl2tocis object

ElbowPlot(cocl2tocis) # The standard deviation seems to really level off at 10

# Recluster with the appropriate number of dimensions
cocl2tocis <- FindNeighbors(cocl2tocis, dims = 1:10)
cocl2tocis <- FindClusters(cocl2tocis, resolution = 0.5)
cocl2tocis <- RunUMAP(cocl2tocis, dims = 1:10)
DimPlot(cocl2tocis, reduction = 'umap')

# Build list of lineages with at least 5 cells in dab tram and check what seurat cluster the cells are in 
cocl2tocis_lin_clust_list <- list()

for (i in fivecell_cDNA$CoCl2toCis){
  temp_df <- as.data.frame(table(Idents(cocl2tocis)[cocl2tocis$Lineage == i]))
  colnames(temp_df) <- c('Seurat_clust', 'Num_cells')
  temp_df$Pcnt_cells <- temp_df$Num_cells/sum(temp_df$Num_cells)
  cocl2tocis_lin_clust_list[[i]] <- temp_df
}

# Need to do a random sampling of the same thing 
cocl2tocis_lin_clust_rand_list <- list()
num_iter <- 800 
for(j in 1:num_iter){
  set.seed(j)
  cocl2tocis_lin_clust_rand_list[[j]] <- list()
  for (i in fivecell_cDNA$CoCl2toCis){
    num_cells <- length(cocl2tocis$Lineage[cocl2tocis$Lineage == i])
    temp_df <- as.data.frame(table(sample(Idents(cocl2tocis), num_cells, replace = F)))
    colnames(temp_df) <- c('Seurat_clust', 'Num_cells')
    temp_df$Pcnt_cells <- temp_df$Num_cells/sum(temp_df$Num_cells)
    cocl2tocis_lin_clust_rand_list[[j]][[i]] <- temp_df
  }
}
```
# Significance testing of the cocl2tocis simulation
```{r}
# Find the mean of the maximum percentage of cells in a single cluster per lineage in each simulation
mean_cocl2tocis <- mean(unlist(lapply(cocl2tocis_lin_clust_list, function(x) max(x$Pcnt_cells))))
means_cocl2tocis_sim <- sapply(1:length(cocl2tocis_lin_clust_rand_list), function(y)
  mean(unlist(lapply(cocl2tocis_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells)))))
z_mean_cocl2tocis <- (mean_cocl2tocis-mean(means_cocl2tocis_sim))/sd(means_cocl2tocis_sim)
pval_mean_cocl2tocis <- pnorm(z_mean_cocl2tocis, mean(means_cocl2tocis_sim), sd(means_cocl2tocis_sim), lower.tail = F)

# Find the weighted mean of the maximum percentage of cells in a single cluster per lineage in each simulation
weighted_mean_cocl2tocis <- weighted.mean(unlist(lapply(cocl2tocis_lin_clust_list, function(x) max(x$Pcnt_cells))),
unlist(lapply(cocl2tocis_lin_clust_list, function(x) sum(x$Num_cells))))
weighted_means_cocl2tocis_sim <- sapply(1:length(cocl2tocis_lin_clust_rand_list), function(y)
  weighted.mean(unlist(lapply(cocl2tocis_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells))),
                unlist(lapply(cocl2tocis_lin_clust_rand_list[[y]], function(x) sum(x$Num_cells)))))
z_wmean_cocl2tocis <- (weighted_mean_cocl2tocis-mean(weighted_means_cocl2tocis_sim))/sd(weighted_means_cocl2tocis_sim)
pval_wmean_cocl2tocis <- pnorm(z_wmean_cocl2tocis, mean(weighted_means_cocl2tocis_sim), sd(weighted_means_cocl2tocis_sim), lower.tail = F)

# Compare each individual distribution of maxes to the observed max by t test and track pval
ttest_pval_cocl2tocis <- c()
for (i in 1:length(means_cocl2tocis_sim)){
  sim_maxes <- unlist(lapply(cocl2tocis_lin_clust_rand_list[[i]], function(x) max(x$Pcnt_cells)))
  ttest_pval_cocl2tocis <- cbind(ttest_pval_cocl2tocis,t.test(x = unlist(lapply(cocl2tocis_lin_clust_list, function(x) max(x$Pcnt_cells))),y = unlist(lapply(cocl2tocis_lin_clust_rand_list[[i]], function(x) max(x$Pcnt_cells))), alternative = 'greater')$p.value)
}

# Save outputs
save(cocl2tocis_lin_clust_list, cocl2tocis_lin_clust_rand_list,z_mean_cocl2tocis,pval_mean_cocl2tocis, z_wmean_cocl2tocis, ttest_pval_cocl2tocis, file = '2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/cocl2tocis_sim_results.RData')
rm(cocl2tocis)
```

# Look at whether lineages cluster together in each individual condition - Starting with CoCl2toDabTram
```{r}
Idents(all_data) <- all_data$OG_condition # Change the idents to the OG condition for subsetting to cocl2todabtram
cocl2todabtram <- subset(all_data, idents = 'cocl2todabtram') # Subset down to the cocl2todabtram object

ElbowPlot(cocl2todabtram) # The standard deviation seems to really level off at 10

# Recluster with the appropriate number of dimensions
cocl2todabtram <- FindNeighbors(cocl2todabtram, dims = 1:10)
cocl2todabtram <- FindClusters(cocl2todabtram, resolution = 0.5)
cocl2todabtram <- RunUMAP(cocl2todabtram, dims = 1:10)
DimPlot(cocl2todabtram, reduction = 'umap')

# Build list of lineages with at least 5 cells in dab tram and check what seurat cluster the cells are in 
cocl2todabtram_lin_clust_list <- list()

for (i in fivecell_cDNA$CoCl2toDabTram){
  temp_df <- as.data.frame(table(Idents(cocl2todabtram)[cocl2todabtram$Lineage == i]))
  colnames(temp_df) <- c('Seurat_clust', 'Num_cells')
  temp_df$Pcnt_cells <- temp_df$Num_cells/sum(temp_df$Num_cells)
  cocl2todabtram_lin_clust_list[[i]] <- temp_df
}

# Need to do a random sampling of the same thing 
cocl2todabtram_lin_clust_rand_list <- list()
num_iter <- 800 
for(j in 1:num_iter){
  set.seed(j)
  cocl2todabtram_lin_clust_rand_list[[j]] <- list()
  for (i in fivecell_cDNA$CoCl2toDabTram){
    num_cells <- length(cocl2todabtram$Lineage[cocl2todabtram$Lineage == i])
    temp_df <- as.data.frame(table(sample(Idents(cocl2todabtram), num_cells, replace = F)))
    colnames(temp_df) <- c('Seurat_clust', 'Num_cells')
    temp_df$Pcnt_cells <- temp_df$Num_cells/sum(temp_df$Num_cells)
    cocl2todabtram_lin_clust_rand_list[[j]][[i]] <- temp_df
  }
}
```
# Significance testing of the cocl2todabtram simulation
```{r}
# Find the mean of the maximum percentage of cells in a single cluster per lineage in each simulation
mean_cocl2todabtram <- mean(unlist(lapply(cocl2todabtram_lin_clust_list, function(x) max(x$Pcnt_cells))))
means_cocl2todabtram_sim <- sapply(1:length(cocl2todabtram_lin_clust_rand_list), function(y)
  mean(unlist(lapply(cocl2todabtram_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells)))))
z_mean_cocl2todabtram <- (mean_cocl2todabtram-mean(means_cocl2todabtram_sim))/sd(means_cocl2todabtram_sim)
pval_mean_cocl2todabtram <- pnorm(z_mean_cocl2todabtram, mean(means_cocl2todabtram_sim), sd(means_cocl2todabtram_sim), lower.tail = F)

# Find the weighted mean of the maximum percentage of cells in a single cluster per lineage in each simulation
weighted_mean_cocl2todabtram <- weighted.mean(unlist(lapply(cocl2todabtram_lin_clust_list, function(x) max(x$Pcnt_cells))),
unlist(lapply(cocl2todabtram_lin_clust_list, function(x) sum(x$Num_cells))))
weighted_means_cocl2todabtram_sim <- sapply(1:length(cocl2todabtram_lin_clust_rand_list), function(y)
  weighted.mean(unlist(lapply(cocl2todabtram_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells))),
                unlist(lapply(cocl2todabtram_lin_clust_rand_list[[y]], function(x) sum(x$Num_cells)))))
z_wmean_cocl2todabtram <- (weighted_mean_cocl2todabtram-mean(weighted_means_cocl2todabtram_sim))/sd(weighted_means_cocl2todabtram_sim)
pval_wmean_cocl2todabtram <- pnorm(z_wmean_cocl2todabtram, mean(weighted_means_cocl2todabtram_sim), sd(weighted_means_cocl2todabtram_sim), lower.tail = F)

# Compare each individual distribution of maxes to the observed max by t test and track pval
ttest_pval_cocl2todabtram <- c()
for (i in 1:length(means_cocl2todabtram_sim)){
  sim_maxes <- unlist(lapply(cocl2todabtram_lin_clust_rand_list[[i]], function(x) max(x$Pcnt_cells)))
  ttest_pval_cocl2todabtram <- cbind(ttest_pval_cocl2todabtram,t.test(x = unlist(lapply(cocl2todabtram_lin_clust_list, function(x) max(x$Pcnt_cells))),y = unlist(lapply(cocl2todabtram_lin_clust_rand_list[[i]], function(x) max(x$Pcnt_cells))), alternative = 'greater')$p.value)
}

# Save outputs
save(cocl2todabtram_lin_clust_list, cocl2todabtram_lin_clust_rand_list,z_mean_cocl2todabtram,pval_mean_cocl2todabtram, z_wmean_cocl2todabtram, ttest_pval_cocl2todabtram, file = '2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/cocl2todabtram_sim_results.RData')
rm(cocl2todabtram)
```

# Look at whether lineages cluster together in each individual condition - Starting with Cis
```{r}
Idents(all_data) <- all_data$OG_condition # Change the idents to the OG condition for subsetting to cis
cis <- subset(all_data, idents = 'cis') # Subset down to the cis object

ElbowPlot(cis) # The standard deviation seems to really level off at 10

# Recluster with the appropriate number of dimensions
cis <- FindNeighbors(cis, dims = 1:10)
cis <- FindClusters(cis, resolution = 0.5)
cis <- RunUMAP(cis, dims = 1:10)
DimPlot(cis, reduction = 'umap')

# Build list of lineages with at least 5 cells in dab tram and check what seurat cluster the cells are in 
cis_lin_clust_list <- list()

for (i in fivecell_cDNA$Cis){
  temp_df <- as.data.frame(table(Idents(cis)[cis$Lineage == i]))
  colnames(temp_df) <- c('Seurat_clust', 'Num_cells')
  temp_df$Pcnt_cells <- temp_df$Num_cells/sum(temp_df$Num_cells)
  cis_lin_clust_list[[i]] <- temp_df
}

# Need to do a random sampling of the same thing 
cis_lin_clust_rand_list <- list()
num_iter <- 800 
for(j in 1:num_iter){
  set.seed(j)
  cis_lin_clust_rand_list[[j]] <- list()
  for (i in fivecell_cDNA$Cis){
    num_cells <- length(cis$Lineage[cis$Lineage == i])
    temp_df <- as.data.frame(table(sample(Idents(cis), num_cells, replace = F)))
    colnames(temp_df) <- c('Seurat_clust', 'Num_cells')
    temp_df$Pcnt_cells <- temp_df$Num_cells/sum(temp_df$Num_cells)
    cis_lin_clust_rand_list[[j]][[i]] <- temp_df
  }
}
```
# Significance testing of the cis simulation
```{r}
# Find the mean of the maximum percentage of cells in a single cluster per lineage in each simulation
mean_cis <- mean(unlist(lapply(cis_lin_clust_list, function(x) max(x$Pcnt_cells))))
means_cis_sim <- sapply(1:length(cis_lin_clust_rand_list), function(y)
  mean(unlist(lapply(cis_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells)))))
z_mean_cis <- (mean_cis-mean(means_cis_sim))/sd(means_cis_sim)
pval_mean_cis <- pnorm(z_mean_cis, mean(means_cis_sim), sd(means_cis_sim), lower.tail = F)

# Find the weighted mean of the maximum percentage of cells in a single cluster per lineage in each simulation
weighted_mean_cis <- weighted.mean(unlist(lapply(cis_lin_clust_list, function(x) max(x$Pcnt_cells))),
unlist(lapply(cis_lin_clust_list, function(x) sum(x$Num_cells))))
weighted_means_cis_sim <- sapply(1:length(cis_lin_clust_rand_list), function(y)
  weighted.mean(unlist(lapply(cis_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells))),
                unlist(lapply(cis_lin_clust_rand_list[[y]], function(x) sum(x$Num_cells)))))
z_wmean_cis <- (weighted_mean_cis-mean(weighted_means_cis_sim))/sd(weighted_means_cis_sim)
pval_wmean_cis <- pnorm(z_wmean_cis, mean(weighted_means_cis_sim), sd(weighted_means_cis_sim), lower.tail = F)

# Compare each individual distribution of maxes to the observed max by t test and track pval
ttest_pval_cis <- c()
for (i in 1:length(means_cis_sim)){
  sim_maxes <- unlist(lapply(cis_lin_clust_rand_list[[i]], function(x) max(x$Pcnt_cells)))
  ttest_pval_cis <- cbind(ttest_pval_cis,t.test(x = unlist(lapply(cis_lin_clust_list, function(x) max(x$Pcnt_cells))),y = unlist(lapply(cis_lin_clust_rand_list[[i]], function(x) max(x$Pcnt_cells))), alternative = 'greater')$p.value)
}

# Save outputs
save(cis_lin_clust_list, cis_lin_clust_rand_list,z_mean_cis,pval_mean_cis, z_wmean_cis, ttest_pval_cis, file = '2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/cis_sim_results.RData')
rm(cis)
```


# Look at whether lineages cluster together in each individual condition - Starting with cistocis
```{r}
Idents(all_data) <- all_data$OG_condition # Change the idents to the OG condition for subsetting to cistocis
cistocis <- subset(all_data, idents = 'cistocis') # Subset down to the cistocis object

ElbowPlot(cistocis) # The standard deviation seems to really level off at 10

# Recluster with the appropriate number of dimensions
cistocis <- FindNeighbors(cistocis, dims = 1:10)
cistocis <- FindClusters(cistocis, resolution = 0.5)
cistocis <- RunUMAP(cistocis, dims = 1:10)
DimPlot(cistocis, reduction = 'umap')

# Build list of lineages with at least 5 cells in dab tram and check what seurat cluster the cells are in 
cistocis_lin_clust_list <- list()

for (i in fivecell_cDNA$CistoCis){
  temp_df <- as.data.frame(table(Idents(cistocis)[cistocis$Lineage == i]))
  colnames(temp_df) <- c('Seurat_clust', 'Num_cells')
  temp_df$Pcnt_cells <- temp_df$Num_cells/sum(temp_df$Num_cells)
  cistocis_lin_clust_list[[i]] <- temp_df
}

# Need to do a random sampling of the same thing 
cistocis_lin_clust_rand_list <- list()
num_iter <- 800 
for(j in 1:num_iter){
  set.seed(j)
  cistocis_lin_clust_rand_list[[j]] <- list()
  for (i in fivecell_cDNA$CistoCis){
    num_cells <- length(cistocis$Lineage[cistocis$Lineage == i])
    temp_df <- as.data.frame(table(sample(Idents(cistocis), num_cells, replace = F)))
    colnames(temp_df) <- c('Seurat_clust', 'Num_cells')
    temp_df$Pcnt_cells <- temp_df$Num_cells/sum(temp_df$Num_cells)
    cistocis_lin_clust_rand_list[[j]][[i]] <- temp_df
  }
}
```
# Significance testing of the cistocis simulation
```{r}
# Find the mean of the maximum percentage of cells in a single cluster per lineage in each simulation
mean_cistocis <- mean(unlist(lapply(cistocis_lin_clust_list, function(x) max(x$Pcnt_cells))))
means_cistocis_sim <- sapply(1:length(cistocis_lin_clust_rand_list), function(y)
  mean(unlist(lapply(cistocis_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells)))))
z_mean_cistocis <- (mean_cistocis-mean(means_cistocis_sim))/sd(means_cistocis_sim)
pval_mean_cistocis <- pnorm(z_mean_cistocis, mean(means_cistocis_sim), sd(means_cistocis_sim), lower.tail = F)

# Find the weighted mean of the maximum percentage of cells in a single cluster per lineage in each simulation
weighted_mean_cistocis <- weighted.mean(unlist(lapply(cistocis_lin_clust_list, function(x) max(x$Pcnt_cells))),
unlist(lapply(cistocis_lin_clust_list, function(x) sum(x$Num_cells))))
weighted_means_cistocis_sim <- sapply(1:length(cistocis_lin_clust_rand_list), function(y)
  weighted.mean(unlist(lapply(cistocis_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells))),
                unlist(lapply(cistocis_lin_clust_rand_list[[y]], function(x) sum(x$Num_cells)))))
z_wmean_cistocis <- (weighted_mean_cistocis-mean(weighted_means_cistocis_sim))/sd(weighted_means_cistocis_sim)
pval_wmean_cistocis <- pnorm(z_wmean_cistocis, mean(weighted_means_cistocis_sim), sd(weighted_means_cistocis_sim), lower.tail = F)

# Compare each individual distribution of maxes to the observed max by t test and track pval
ttest_pval_cistocis <- c()
for (i in 1:length(means_cistocis_sim)){
  sim_maxes <- unlist(lapply(cistocis_lin_clust_rand_list[[i]], function(x) max(x$Pcnt_cells)))
  ttest_pval_cistocis <- cbind(ttest_pval_cistocis,t.test(x = unlist(lapply(cistocis_lin_clust_list, function(x) max(x$Pcnt_cells))),y = unlist(lapply(cistocis_lin_clust_rand_list[[i]], function(x) max(x$Pcnt_cells))), alternative = 'greater')$p.value)
}

# Save outputs
save(cistocis_lin_clust_list, cistocis_lin_clust_rand_list,z_mean_cistocis,pval_mean_cistocis, z_wmean_cistocis, ttest_pval_cistocis, file = '2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/cistocis_sim_results.RData')
rm(cistocis)
```


# Look at whether lineages cluster together in each individual condition - Starting with cistodabtram
```{r}
Idents(all_data) <- all_data$OG_condition # Change the idents to the OG condition for subsetting to cistodabtram
cistodabtram <- subset(all_data, idents = 'cistodabtram') # Subset down to the cistodabtram object

ElbowPlot(cistodabtram) # The standard deviation seems to really level off at 10

# Recluster with the appropriate number of dimensions
cistodabtram <- FindNeighbors(cistodabtram, dims = 1:10)
cistodabtram <- FindClusters(cistodabtram, resolution = 0.5)
cistodabtram <- RunUMAP(cistodabtram, dims = 1:10)
DimPlot(cistodabtram, reduction = 'umap')

# Build list of lineages with at least 5 cells in dab tram and check what seurat cluster the cells are in 
cistodabtram_lin_clust_list <- list()

for (i in fivecell_cDNA$CistoDabTram){
  temp_df <- as.data.frame(table(Idents(cistodabtram)[cistodabtram$Lineage == i]))
  colnames(temp_df) <- c('Seurat_clust', 'Num_cells')
  temp_df$Pcnt_cells <- temp_df$Num_cells/sum(temp_df$Num_cells)
  cistodabtram_lin_clust_list[[i]] <- temp_df
}

# Need to do a random sampling of the same thing 
cistodabtram_lin_clust_rand_list <- list()
num_iter <- 800 
for(j in 1:num_iter){
  set.seed(j)
  cistodabtram_lin_clust_rand_list[[j]] <- list()
  for (i in fivecell_cDNA$CistoDabTram){
    num_cells <- length(cistodabtram$Lineage[cistodabtram$Lineage == i])
    temp_df <- as.data.frame(table(sample(Idents(cistodabtram), num_cells, replace = F)))
    colnames(temp_df) <- c('Seurat_clust', 'Num_cells')
    temp_df$Pcnt_cells <- temp_df$Num_cells/sum(temp_df$Num_cells)
    cistodabtram_lin_clust_rand_list[[j]][[i]] <- temp_df
  }
}
```
# Significance testing of the cistodabtram simulation
```{r}
# Find the mean of the maximum percentage of cells in a single cluster per lineage in each simulation
mean_cistodabtram <- mean(unlist(lapply(cistodabtram_lin_clust_list, function(x) max(x$Pcnt_cells))))
means_cistodabtram_sim <- sapply(1:length(cistodabtram_lin_clust_rand_list), function(y)
  mean(unlist(lapply(cistodabtram_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells)))))
z_mean_cistodabtram <- (mean_cistodabtram-mean(means_cistodabtram_sim))/sd(means_cistodabtram_sim)
pval_mean_cistodabtram <- pnorm(z_mean_cistodabtram, mean(means_cistodabtram_sim), sd(means_cistodabtram_sim), lower.tail = F)

# Find the weighted mean of the maximum percentage of cells in a single cluster per lineage in each simulation
weighted_mean_cistodabtram <- weighted.mean(unlist(lapply(cistodabtram_lin_clust_list, function(x) max(x$Pcnt_cells))),
unlist(lapply(cistodabtram_lin_clust_list, function(x) sum(x$Num_cells))))
weighted_means_cistodabtram_sim <- sapply(1:length(cistodabtram_lin_clust_rand_list), function(y)
  weighted.mean(unlist(lapply(cistodabtram_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells))),
                unlist(lapply(cistodabtram_lin_clust_rand_list[[y]], function(x) sum(x$Num_cells)))))
z_wmean_cistodabtram <- (weighted_mean_cistodabtram-mean(weighted_means_cistodabtram_sim))/sd(weighted_means_cistodabtram_sim)
pval_wmean_cistodabtram <- pnorm(z_wmean_cistodabtram, mean(weighted_means_cistodabtram_sim), sd(weighted_means_cistodabtram_sim), lower.tail = F)

# Compare each individual distribution of maxes to the observed max by t test and track pval
ttest_pval_cistodabtram <- c()
for (i in 1:length(means_cistodabtram_sim)){
  sim_maxes <- unlist(lapply(cistodabtram_lin_clust_rand_list[[i]], function(x) max(x$Pcnt_cells)))
  ttest_pval_cistodabtram <- cbind(ttest_pval_cistodabtram,t.test(x = unlist(lapply(cistodabtram_lin_clust_list, function(x) max(x$Pcnt_cells))),y = unlist(lapply(cistodabtram_lin_clust_rand_list[[i]], function(x) max(x$Pcnt_cells))), alternative = 'greater')$p.value)
}

# Save outputs
save(cistodabtram_lin_clust_list, cistodabtram_lin_clust_rand_list,z_mean_cistodabtram,pval_mean_cistodabtram, z_wmean_cistodabtram, ttest_pval_cistodabtram, file = '2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/cistodabtram_sim_results.RData')
rm(cistodabtram)
```

# Look at whether lineages cluster together in each individual condition - Starting with cistococl2
```{r}
Idents(all_data) <- all_data$OG_condition # Change the idents to the OG condition for subsetting to cistococl2
cistococl2 <- subset(all_data, idents = 'cistococl2') # Subset down to the cistococl2 object

ElbowPlot(cistococl2) # The standard deviation seems to really level off at 10

# Recluster with the appropriate number of dimensions
cistococl2 <- FindNeighbors(cistococl2, dims = 1:10)
cistococl2 <- FindClusters(cistococl2, resolution = 0.5)
cistococl2 <- RunUMAP(cistococl2, dims = 1:10)
DimPlot(cistococl2, reduction = 'umap')

# Build list of lineages with at least 5 cells in dab tram and check what seurat cluster the cells are in 
cistococl2_lin_clust_list <- list()

for (i in fivecell_cDNA$CistoCoCl2){
  temp_df <- as.data.frame(table(Idents(cistococl2)[cistococl2$Lineage == i]))
  colnames(temp_df) <- c('Seurat_clust', 'Num_cells')
  temp_df$Pcnt_cells <- temp_df$Num_cells/sum(temp_df$Num_cells)
  cistococl2_lin_clust_list[[i]] <- temp_df
}

# Need to do a random sampling of the same thing 
cistococl2_lin_clust_rand_list <- list()
num_iter <- 800 
for(j in 1:num_iter){
  set.seed(j)
  cistococl2_lin_clust_rand_list[[j]] <- list()
  for (i in fivecell_cDNA$CistoCoCl2){
    num_cells <- length(cistococl2$Lineage[cistococl2$Lineage == i])
    temp_df <- as.data.frame(table(sample(Idents(cistococl2), num_cells, replace = F)))
    colnames(temp_df) <- c('Seurat_clust', 'Num_cells')
    temp_df$Pcnt_cells <- temp_df$Num_cells/sum(temp_df$Num_cells)
    cistococl2_lin_clust_rand_list[[j]][[i]] <- temp_df
  }
}
```
# Significance testing of the cistococl2 simulation
```{r}
# Find the mean of the maximum percentage of cells in a single cluster per lineage in each simulation
mean_cistococl2 <- mean(unlist(lapply(cistococl2_lin_clust_list, function(x) max(x$Pcnt_cells))))
means_cistococl2_sim <- sapply(1:length(cistococl2_lin_clust_rand_list), function(y)
  mean(unlist(lapply(cistococl2_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells)))))
z_mean_cistococl2 <- (mean_cistococl2-mean(means_cistococl2_sim))/sd(means_cistococl2_sim)
pval_mean_cistococl2 <- pnorm(z_mean_cistococl2, mean(means_cistococl2_sim), sd(means_cistococl2_sim), lower.tail = F)

# Find the weighted mean of the maximum percentage of cells in a single cluster per lineage in each simulation
weighted_mean_cistococl2 <- weighted.mean(unlist(lapply(cistococl2_lin_clust_list, function(x) max(x$Pcnt_cells))),
unlist(lapply(cistococl2_lin_clust_list, function(x) sum(x$Num_cells))))
weighted_means_cistococl2_sim <- sapply(1:length(cistococl2_lin_clust_rand_list), function(y)
  weighted.mean(unlist(lapply(cistococl2_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells))),
                unlist(lapply(cistococl2_lin_clust_rand_list[[y]], function(x) sum(x$Num_cells)))))
z_wmean_cistococl2 <- (weighted_mean_cistococl2-mean(weighted_means_cistococl2_sim))/sd(weighted_means_cistococl2_sim)
pval_wmean_cistococl2 <- pnorm(z_wmean_cistococl2, mean(weighted_means_cistococl2_sim), sd(weighted_means_cistococl2_sim), lower.tail = F)

# Compare each individual distribution of maxes to the observed max by t test and track pval
ttest_pval_cistococl2 <- c()
for (i in 1:length(means_cistococl2_sim)){
  sim_maxes <- unlist(lapply(cistococl2_lin_clust_rand_list[[i]], function(x) max(x$Pcnt_cells)))
  ttest_pval_cistococl2 <- cbind(ttest_pval_cistococl2,t.test(x = unlist(lapply(cistococl2_lin_clust_list, function(x) max(x$Pcnt_cells))),y = unlist(lapply(cistococl2_lin_clust_rand_list[[i]], function(x) max(x$Pcnt_cells))), alternative = 'greater')$p.value)
}

# Save outputs
save(cistococl2_lin_clust_list, cistococl2_lin_clust_rand_list,z_mean_cistococl2,pval_mean_cistococl2, z_wmean_cistococl2, ttest_pval_cistococl2, file = '2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/cistococl2_sim_results.RData')
rm(cistococl2)
```

# Build dataframe of overall simulation results
```{r}
sim_results <- rbind(
  dabtram <- data.frame("Mean_Zscore" = z_mean_dabtram, "Mean_pval" = pval_mean_dabtram, "Weighted_Mean_Zscore" = z_wmean_dabtram, "Weighted_Mean_pval" = pval_wmean_dabtram, ttest_sims_sig = sum(ttest_pval_dabtram <= 0.05)),
  dabtramtodabtram <- data.frame("Mean_Zscore" = z_mean_dabtramtodabtram, "Mean_pval" = pval_mean_dabtramtodabtram, "Weighted_Mean_Zscore" = z_wmean_dabtramtodabtram, "Weighted_Mean_pval" = pval_wmean_dabtramtodabtram, ttest_sims_sig = sum(ttest_pval_dabtramtodabtram <= 0.05)),
  dabtramtococl2 <- data.frame("Mean_Zscore" = z_mean_dabtramtococl2, "Mean_pval" = pval_mean_dabtramtococl2, "Weighted_Mean_Zscore" = z_wmean_dabtramtococl2, "Weighted_Mean_pval" = pval_wmean_dabtramtococl2, ttest_sims_sig = sum(ttest_pval_dabtramtococl2 <= 0.05)),
  dabtramtocis <- data.frame("Mean_Zscore" = z_mean_dabtramtocis, "Mean_pval" = pval_mean_dabtramtocis, "Weighted_Mean_Zscore" = z_wmean_dabtramtocis, "Weighted_Mean_pval" = pval_wmean_dabtramtocis, ttest_sims_sig = sum(ttest_pval_dabtramtocis <= 0.05)),
  cis <- data.frame("Mean_Zscore" = z_mean_cis, "Mean_pval" = pval_mean_cis, "Weighted_Mean_Zscore" = z_wmean_cis, "Weighted_Mean_pval" = pval_wmean_cis, ttest_sims_sig = sum(ttest_pval_cis <= 0.05)),
  cistodabtram <- data.frame("Mean_Zscore" = z_mean_cistodabtram, "Mean_pval" = pval_mean_cistodabtram, "Weighted_Mean_Zscore" = z_wmean_cistodabtram, "Weighted_Mean_pval" = pval_wmean_cistodabtram, ttest_sims_sig = sum(ttest_pval_cistodabtram <= 0.05)),
  cistococl2 <- data.frame("Mean_Zscore" = z_mean_cistococl2, "Mean_pval" = pval_mean_cistococl2, "Weighted_Mean_Zscore" = z_wmean_cistococl2, "Weighted_Mean_pval" = pval_wmean_cistococl2, ttest_sims_sig = sum(ttest_pval_cistococl2 <= 0.05)),
  cistocis <- data.frame("Mean_Zscore" = z_mean_cistocis, "Mean_pval" = pval_mean_cistocis, "Weighted_Mean_Zscore" = z_wmean_cistocis, "Weighted_Mean_pval" = pval_wmean_cistocis, ttest_sims_sig = sum(ttest_pval_cistocis <= 0.05)),
  cocl2 <- data.frame("Mean_Zscore" = z_mean_cocl2, "Mean_pval" = pval_mean_cocl2, "Weighted_Mean_Zscore" = z_wmean_cocl2, "Weighted_Mean_pval" = pval_wmean_cocl2, ttest_sims_sig = sum(ttest_pval_cocl2 <= 0.05)),
  cocl2todabtram <- data.frame("Mean_Zscore" = z_mean_cocl2todabtram, "Mean_pval" = pval_mean_cocl2todabtram, "Weighted_Mean_Zscore" = z_wmean_cocl2todabtram, "Weighted_Mean_pval" = pval_wmean_cocl2todabtram, ttest_sims_sig = sum(ttest_pval_cocl2todabtram <= 0.05)),
  cocl2tococl2 <- data.frame("Mean_Zscore" = z_mean_cocl2tococl2, "Mean_pval" = pval_mean_cocl2tococl2, "Weighted_Mean_Zscore" = z_wmean_cocl2tococl2, "Weighted_Mean_pval" = pval_wmean_cocl2tococl2, ttest_sims_sig = sum(ttest_pval_cocl2tococl2 <= 0.05)),
  cocl2tocis <- data.frame("Mean_Zscore" = z_mean_cocl2tocis, "Mean_pval" = pval_mean_cocl2tocis, "Weighted_Mean_Zscore" = z_wmean_cocl2tocis, "Weighted_Mean_pval" = pval_wmean_cocl2tocis, ttest_sims_sig = sum(ttest_pval_cocl2tocis <= 0.05))
)

rownames(sim_results) <- c("dabtram", "dabtramtodabtram", "dabtramtococl2", "dabtramtocis", "cis", "cistodabtram", "cistococl2", "cistocis", "cocl2", "cocl2todabtram", "cocl2tococl2", "cocl2tocis")

write.csv(sim_results, '2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/sim_results.csv', row.names = T) 
```

# Load simulation data back in
```{r}
sim_results <- read.csv('2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/sim_results.csv')
load('2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/dabtram_sim_results.RData')
load('2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/dabtramtodabtram_sim_results.RData')
load('2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/dabtramtococl2_sim_results.RData')
load('2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/dabtramtocis_sim_results.RData')
load('2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/cocl2_sim_results.RData')
load('2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/cocl2todabtram_sim_results.RData')
load('2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/cocl2tococl2_sim_results.RData')
load('2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/cocl2tocis_sim_results.RData')
load('2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/cis_sim_results.RData')
load('2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/cistodabtram_sim_results.RData')
load('2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/cistococl2_sim_results.RData')
load('2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/cistocis_sim_results.RData')

```

# Plot heatmaps of true percentage of cells per lineage in each cluster vs random simulations
```{r}

breaks <- seq(0, 1, 0.1)

# DabTram
pdf('2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/proportion_clusters_heatmaps.pdf', width = 16)
dabtram_pcnts <- as.data.frame(sapply(dabtram_lin_clust_list, '[[', 3))
cell_nums <- sapply(dabtram_lin_clust_list, function(x) sum(x$Num_cells))
rownames(dabtram_pcnts) <- c("0","1","2","3","4","5","6")
colnames(dabtram_pcnts) <- paste(names(cell_nums), rep('-',length(cell_nums)), cell_nums, rep('cells', length(cell_nums)))
p <- pheatmap(dabtram_pcnts,cluster_rows = F, color = inferno(11), main = "DabTram", breaks = breaks) # 
p

dabtram_pcnts_rand <- as.data.frame(Reduce(`+`, lapply(dabtram_lin_clust_rand_list, function(x) {
  sapply(x, function(y) y$Pcnt_cells)
}))/ length(dabtram_lin_clust_rand_list))
rownames(dabtram_pcnts_rand) <- c("0","1","2","3","4","5","6")
colnames(dabtram_pcnts_rand) <- paste(names(cell_nums), rep('-',length(cell_nums)), cell_nums, rep('cells', length(cell_nums)))
dabtram_pcnts_rand <- dabtram_pcnts_rand[,p$tree_col$order]
pheatmap(dabtram_pcnts_rand,cluster_rows = F, color = inferno(11), main = "DabTram Rand", breaks = breaks, cluster_cols = F)

# DabTram to DabTram
dabtramtodabtram_pcnts <- as.data.frame(sapply(dabtramtodabtram_lin_clust_list, '[[', 3))
cell_nums <- sapply(dabtramtodabtram_lin_clust_list, function(x) sum(x$Num_cells))
rownames(dabtramtodabtram_pcnts) <- c("0","1","2","3","4","5","6")
colnames(dabtramtodabtram_pcnts) <- paste(names(cell_nums), rep('-',length(cell_nums)), cell_nums, rep('cells', length(cell_nums)))
p <- pheatmap(dabtramtodabtram_pcnts,cluster_rows = F, color = inferno(11), main = "DabTram to DabTram", breaks = breaks)
p

dabtramtodabtram_pcnts_rand <- as.data.frame(Reduce(`+`, lapply(dabtramtodabtram_lin_clust_rand_list, function(x) {
  sapply(x, function(y) y$Pcnt_cells)
}))/ length(dabtramtodabtram_lin_clust_rand_list))
rownames(dabtramtodabtram_pcnts_rand) <- c("0","1","2","3","4","5","6")
colnames(dabtramtodabtram_pcnts_rand) <- paste(names(cell_nums), rep('-',length(cell_nums)), cell_nums, rep('cells', length(cell_nums)))
dabtramtodabtram_pcnts_rand <- dabtramtodabtram_pcnts_rand[,p$tree_col$order]
pheatmap(dabtramtodabtram_pcnts_rand,cluster_rows = F, color = inferno(11), main = "DabTram to DabTram Rand", breaks = breaks, cluster_cols = F)

# DabTram to CoCl2
dabtramtococl2_pcnts <- as.data.frame(sapply(dabtramtococl2_lin_clust_list, '[[', 3))
cell_nums <- sapply(dabtramtococl2_lin_clust_list, function(x) sum(x$Num_cells))
rownames(dabtramtococl2_pcnts) <- c("0","1","2","3","4","5","6","7")
colnames(dabtramtococl2_pcnts) <- paste(names(cell_nums), rep('-',length(cell_nums)), cell_nums, rep('cells', length(cell_nums)))
p <- pheatmap(dabtramtococl2_pcnts,cluster_rows = F, color = inferno(11), main = "DabTram to CoCl2", breaks = breaks)
p

dabtramtococl2_pcnts_rand <- as.data.frame(Reduce(`+`, lapply(dabtramtococl2_lin_clust_rand_list, function(x) {
  sapply(x, function(y) y$Pcnt_cells)
}))/ length(dabtramtococl2_lin_clust_rand_list))
rownames(dabtramtococl2_pcnts_rand) <- c("0","1","2","3","4","5","6","7")
colnames(dabtramtococl2_pcnts_rand) <- paste(names(cell_nums), rep('-',length(cell_nums)), cell_nums, rep('cells', length(cell_nums)))
dabtramtococl2_pcnts_rand <- dabtramtococl2_pcnts_rand[,p$tree_col$order]
pheatmap(dabtramtococl2_pcnts_rand,cluster_rows = F, color = inferno(11), main = "DabTram to CoCl2 Rand", breaks = breaks, cluster_cols = F)

# DabTram to Cis
dabtramtocis_pcnts <- as.data.frame(sapply(dabtramtocis_lin_clust_list, '[[', 3))
cell_nums <- sapply(dabtramtocis_lin_clust_list, function(x) sum(x$Num_cells))
rownames(dabtramtocis_pcnts) <- c("0","1","2","3","4","5","6")
colnames(dabtramtocis_pcnts) <- paste(names(cell_nums), rep('-',length(cell_nums)), cell_nums, rep('cells', length(cell_nums)))
p <- pheatmap(dabtramtocis_pcnts,cluster_rows = F, color = inferno(11), main = "DabTram to Cis", breaks = breaks)
p

dabtramtocis_pcnts_rand <- as.data.frame(Reduce(`+`, lapply(dabtramtocis_lin_clust_rand_list, function(x) {
  sapply(x, function(y) y$Pcnt_cells)
}))/ length(dabtramtocis_lin_clust_rand_list))
rownames(dabtramtocis_pcnts_rand) <- c("0","1","2","3","4","5","6")
colnames(dabtramtocis_pcnts_rand) <- paste(names(cell_nums), rep('-',length(cell_nums)), cell_nums, rep('cells', length(cell_nums)))
dabtramtocis_pcnts_rand <- dabtramtocis_pcnts_rand[,p$tree_col$order]
pheatmap(dabtramtocis_pcnts_rand,cluster_rows = F, color = inferno(11), main = "DabTram to Cis Rand", breaks = breaks, cluster_cols = F)

# CoCl2 objects
cocl2_pcnts <- as.data.frame(sapply(cocl2_lin_clust_list, '[[', 3))
cell_nums <- sapply(cocl2_lin_clust_list, function(x) sum(x$Num_cells))
rownames(cocl2_pcnts) <- c("0","1","2","3","4","5","6","7","8")
colnames(cocl2_pcnts) <- paste(names(cell_nums), rep('-',length(cell_nums)), cell_nums, rep('cells', length(cell_nums)))
p <- pheatmap(cocl2_pcnts,cluster_rows = F, color = inferno(11), main = "CoCl2", breaks = breaks)
p

cocl2_pcnts_rand <- as.data.frame(Reduce(`+`, lapply(cocl2_lin_clust_rand_list, function(x) {
  sapply(x, function(y) y$Pcnt_cells)
}))/ length(cocl2_lin_clust_rand_list))
rownames(cocl2_pcnts_rand) <- c("0","1","2","3","4","5","6","7","8")
colnames(cocl2_pcnts_rand) <- paste(names(cell_nums), rep('-',length(cell_nums)), cell_nums, rep('cells', length(cell_nums)))
cocl2_pcnts_rand <- cocl2_pcnts_rand[,p$tree_col$order]
pheatmap(cocl2_pcnts_rand,cluster_rows = F, color = inferno(11), main = "CoCl2 Rand", breaks = breaks, cluster_cols = F)

# CoCl2 to DabTram
cocl2todabtram_pcnts <- as.data.frame(sapply(cocl2todabtram_lin_clust_list, '[[', 3))
cell_nums <- sapply(cocl2todabtram_lin_clust_list, function(x) sum(x$Num_cells))
rownames(cocl2todabtram_pcnts) <- c("0","1","2","3","4","5","6")
colnames(cocl2todabtram_pcnts) <- paste(names(cell_nums), rep('-',length(cell_nums)), cell_nums, rep('cells', length(cell_nums)))
p <- pheatmap(cocl2todabtram_pcnts,cluster_rows = F, color = inferno(11), main = "CoCl2 to DabTram", breaks = breaks)
p

cocl2todabtram_pcnts_rand <- as.data.frame(Reduce(`+`, lapply(cocl2todabtram_lin_clust_rand_list, function(x) {
  sapply(x, function(y) y$Pcnt_cells)
}))/ length(cocl2todabtram_lin_clust_rand_list))
rownames(cocl2todabtram_pcnts_rand) <- c("0","1","2","3","4","5","6")
colnames(cocl2todabtram_pcnts_rand) <- paste(names(cell_nums), rep('-',length(cell_nums)), cell_nums, rep('cells', length(cell_nums)))
cocl2todabtram_pcnts_rand <- cocl2todabtram_pcnts_rand[,p$tree_col$order]
pheatmap(cocl2todabtram_pcnts_rand,cluster_rows = F, color = inferno(11), main = "CoCl2 to DabTram Rand", breaks = breaks, cluster_cols = F)

# CoCl2 to CoCl2
cocl2tococl2_pcnts <- as.data.frame(sapply(cocl2tococl2_lin_clust_list, '[[', 3))
cell_nums <- sapply(cocl2tococl2_lin_clust_list, function(x) sum(x$Num_cells))
rownames(cocl2tococl2_pcnts) <- c("0","1","2","3","4","5")
colnames(cocl2tococl2_pcnts) <- paste(names(cell_nums), rep('-',length(cell_nums)), cell_nums, rep('cells', length(cell_nums)))
p <- pheatmap(cocl2tococl2_pcnts,cluster_rows = F, color = inferno(11), main = "CoCl2 to CoCl2", breaks = breaks)
p

cocl2tococl2_pcnts_rand <- as.data.frame(Reduce(`+`, lapply(cocl2tococl2_lin_clust_rand_list, function(x) {
  sapply(x, function(y) y$Pcnt_cells)
}))/ length(cocl2tococl2_lin_clust_rand_list))
rownames(cocl2tococl2_pcnts_rand) <- c("0","1","2","3","4","5")
colnames(cocl2tococl2_pcnts_rand) <- paste(names(cell_nums), rep('-',length(cell_nums)), cell_nums, rep('cells', length(cell_nums)))
cocl2tococl2_pcnts_rand <- cocl2tococl2_pcnts_rand[,p$tree_col$order]
pheatmap(cocl2tococl2_pcnts_rand,cluster_rows = F, color = inferno(11), main = "CoCl2 to CoCl2 Rand", breaks = breaks, cluster_cols = F)

# CoCl2 to Cis
cocl2tocis_pcnts <- as.data.frame(sapply(cocl2tocis_lin_clust_list, '[[', 3))
cell_nums <- sapply(cocl2tocis_lin_clust_list, function(x) sum(x$Num_cells))
rownames(cocl2tocis_pcnts) <- c("0","1","2","3","4","5")
colnames(cocl2tocis_pcnts) <- paste(names(cell_nums), rep('-',length(cell_nums)), cell_nums, rep('cells', length(cell_nums)))
p <- pheatmap(cocl2tocis_pcnts,cluster_rows = F, color = inferno(11), main = "CoCl2 to Cis", breaks = breaks)
p

cocl2tocis_pcnts_rand <- as.data.frame(Reduce(`+`, lapply(cocl2tocis_lin_clust_rand_list, function(x) {
  sapply(x, function(y) y$Pcnt_cells)
}))/ length(cocl2tocis_lin_clust_rand_list))
rownames(cocl2tocis_pcnts_rand) <- c("0","1","2","3","4","5")
colnames(cocl2tocis_pcnts_rand) <- paste(names(cell_nums), rep('-',length(cell_nums)), cell_nums, rep('cells', length(cell_nums)))
cocl2tocis_pcnts_rand <- cocl2tocis_pcnts_rand[,p$tree_col$order]
pheatmap(cocl2tocis_pcnts_rand,cluster_rows = F, color = inferno(11), main = "CoCl2 to Cis Rand", breaks = breaks, cluster_cols = F)

# Cis 
cis_pcnts <- as.data.frame(sapply(cis_lin_clust_list, '[[', 3))
cell_nums <- sapply(cis_lin_clust_list, function(x) sum(x$Num_cells))
rownames(cis_pcnts) <- c("0","1","2","3","4","5","6","7")
colnames(cis_pcnts) <- paste(names(cell_nums), rep('-',length(cell_nums)), cell_nums, rep('cells', length(cell_nums)))
p <- pheatmap(cis_pcnts,cluster_rows = F, color = inferno(10), main = "Cis", breaks = breaks)
p

cis_pcnts_rand <- as.data.frame(Reduce(`+`, lapply(cis_lin_clust_rand_list, function(x) {
  sapply(x, function(y) y$Pcnt_cells)
}))/ length(cis_lin_clust_rand_list))
rownames(cis_pcnts_rand) <- c("0","1","2","3","4","5","6","7")
colnames(cis_pcnts_rand) <- paste(names(cell_nums), rep('-',length(cell_nums)), cell_nums, rep('cells', length(cell_nums)))
cis_pcnts_rand <- cis_pcnts_rand[,p$tree_col$order]
pheatmap(cis_pcnts_rand,cluster_rows = F, color = inferno(11), main = "Cis Rand", breaks = breaks, cluster_cols = F)

# Cis to DabTram
cistodabtram_pcnts <- as.data.frame(sapply(cistodabtram_lin_clust_list, '[[', 3))
cell_nums <- sapply(cistodabtram_lin_clust_list, function(x) sum(x$Num_cells))
rownames(cistodabtram_pcnts) <- c("0","1","2","3","4","5","6","7")
colnames(cistodabtram_pcnts) <- paste(names(cell_nums), rep('-',length(cell_nums)), cell_nums, rep('cells', length(cell_nums)))
p <- pheatmap(cistodabtram_pcnts,cluster_rows = F, color = inferno(11), main = "Cis to DabTram", breaks = breaks)
p

cistodabtram_pcnts_rand <- as.data.frame(Reduce(`+`, lapply(cistodabtram_lin_clust_rand_list, function(x) {
  sapply(x, function(y) y$Pcnt_cells)
}))/ length(cistodabtram_lin_clust_rand_list))
rownames(cistodabtram_pcnts_rand) <- c("0","1","2","3","4","5","6","7")
colnames(cistodabtram_pcnts_rand) <- paste(names(cell_nums), rep('-',length(cell_nums)), cell_nums, rep('cells', length(cell_nums)))
cistodabtram_pcnts_rand <- cistodabtram_pcnts_rand[,p$tree_col$order]
pheatmap(cistodabtram_pcnts_rand,cluster_rows = F, color = inferno(11), main = "Cis to DabTram Rand", breaks = breaks, cluster_cols = F)

# Cis to CoCl2
cistococl2_pcnts <- as.data.frame(sapply(cistococl2_lin_clust_list, '[[', 3))
cell_nums <- sapply(cistococl2_lin_clust_list, function(x) sum(x$Num_cells))
rownames(cistococl2_pcnts) <- c("0","1","2","3","4","5","6")
colnames(cistococl2_pcnts) <- paste(names(cell_nums), rep('-',length(cell_nums)), cell_nums, rep('cells', length(cell_nums)))
p <- pheatmap(cistococl2_pcnts,cluster_rows = F, color = inferno(11), main = "Cis to CoCl2", breaks = breaks)
p

cistococl2_pcnts_rand <- as.data.frame(Reduce(`+`, lapply(cistococl2_lin_clust_rand_list, function(x) {
  sapply(x, function(y) y$Pcnt_cells)
}))/ length(cistococl2_lin_clust_rand_list))
rownames(cistococl2_pcnts_rand) <- c("0","1","2","3","4","5","6")
colnames(cistococl2_pcnts_rand) <- paste(names(cell_nums), rep('-',length(cell_nums)), cell_nums, rep('cells', length(cell_nums)))
cistococl2_pcnts_rand <- cistococl2_pcnts_rand[,p$tree_col$order]
pheatmap(cistococl2_pcnts_rand,cluster_rows = F, color = inferno(11), main = "Cis to CoCl2 Rand", breaks = breaks, cluster_cols = F)

# Cis to Cis
cistocis_pcnts <- as.data.frame(sapply(cistocis_lin_clust_list, '[[', 3))
cell_nums <- sapply(cistocis_lin_clust_list, function(x) sum(x$Num_cells))
rownames(cistocis_pcnts) <- c("0","1","2","3","4","5","6","7","8")
colnames(cistocis_pcnts) <- paste(names(cell_nums), rep('-',length(cell_nums)), cell_nums, rep('cells', length(cell_nums)))
p <- pheatmap(cistocis_pcnts,cluster_rows = F, color = inferno(11), main = "Cis to Cis", breaks = breaks)
p

cistocis_pcnts_rand <- as.data.frame(Reduce(`+`, lapply(cistocis_lin_clust_rand_list, function(x) {
  sapply(x, function(y) y$Pcnt_cells)
}))/ length(cistocis_lin_clust_rand_list))
rownames(cistocis_pcnts_rand) <- c("0","1","2","3","4","5","6","7","8")
colnames(cistocis_pcnts_rand) <- paste(names(cell_nums), rep('-',length(cell_nums)), cell_nums, rep('cells', length(cell_nums)))
cistocis_pcnts_rand <- cistocis_pcnts_rand[,p$tree_col$order]
pheatmap(cistocis_pcnts_rand,cluster_rows = F, color = inferno(11), main = "Cis to Cis Rand", breaks = breaks, cluster_cols = F)
dev.off()

```

# plot the different test statistics - histogram of weighted means of maximum contribution to lineage vs true
```{r}

pdf('2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/weighted_mean_cluster_assignments_test_stats.pdf')
# DabTram
# Find the weighted mean of the maximum percentage of cells in a single cluster per lineage in each simulation
wmean <- weighted.mean(unlist(lapply(dabtram_lin_clust_list, function(x) max(x$Pcnt_cells))),
unlist(lapply(dabtram_lin_clust_list, function(x) sum(x$Num_cells))))
df <- data.frame(weighted_mean = sapply(1:length(dabtram_lin_clust_rand_list), function(y)
  weighted.mean(unlist(lapply(dabtram_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells))),
                unlist(lapply(dabtram_lin_clust_rand_list[[y]], function(x) sum(x$Num_cells))))))

ggplot(df, aes(x = weighted_mean))+geom_histogram(fill = '#623594', color = 'black', binwidth = 0.005) + geom_vline(xintercept = wmean, linetype = 'dashed', color = 'red') + geom_text(aes(x = wmean+.03, label = round(wmean, 3), y = 500), color = 'red') + labs(y = 'Number of simulations', x = 'Weighted mean of percent of cells in dominant lineage', title = 'DabTram') + xlim(0.15, 0.65) + ylim(0,850) + geom_text(aes(x = wmean+.03, label = 'Observed', y = 525), color = 'red') + theme_classic()

# DabTram to DabTram
# Find the weighted mean of the maximum percentage of cells in a single cluster per lineage in each simulation
wmean <- weighted.mean(unlist(lapply(dabtramtodabtram_lin_clust_list, function(x) max(x$Pcnt_cells))),
unlist(lapply(dabtramtodabtram_lin_clust_list, function(x) sum(x$Num_cells))))
df <- data.frame(weighted_mean = sapply(1:length(dabtramtodabtram_lin_clust_rand_list), function(y)
  weighted.mean(unlist(lapply(dabtramtodabtram_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells))),
                unlist(lapply(dabtramtodabtram_lin_clust_rand_list[[y]], function(x) sum(x$Num_cells))))))

ggplot(df, aes(x = weighted_mean))+geom_histogram(fill = '#561e59', color = 'black', binwidth = 0.005) + geom_vline(xintercept = wmean, linetype = 'dashed', color = 'red') + geom_text(aes(x = wmean+.03, label = round(wmean, 3), y = 500), color = 'red') + labs(y = 'Number of simulations', x = 'Weighted mean of percent of cells in dominant lineage', title = 'DabTram to DabTram') + xlim(0.15, 0.65) + ylim(0,850) + geom_text(aes(x = wmean+.03, label = 'Observed', y = 525), color = 'red') + theme_classic()

# DabTram to CoCl2
# Find the weighted mean of the maximum percentage of cells in a single cluster per lineage in each simulation
wmean <- weighted.mean(unlist(lapply(dabtramtococl2_lin_clust_list, function(x) max(x$Pcnt_cells))),
unlist(lapply(dabtramtococl2_lin_clust_list, function(x) sum(x$Num_cells))))
df <- data.frame(weighted_mean = sapply(1:length(dabtramtococl2_lin_clust_rand_list), function(y)
  weighted.mean(unlist(lapply(dabtramtococl2_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells))),
                unlist(lapply(dabtramtococl2_lin_clust_rand_list[[y]], function(x) sum(x$Num_cells))))))

ggplot(df, aes(x = weighted_mean))+geom_histogram(fill = '#A2248E', color = 'black', binwidth = 0.005) + geom_vline(xintercept = wmean, linetype = 'dashed', color = 'red') + geom_text(aes(x = wmean+.03, label = round(wmean, 3), y = 500), color = 'red') + labs(y = 'Number of simulations', x = 'Weighted mean of percent of cells in dominant lineage', title = 'DabTram to CoCl2') + xlim(0.15, 0.65) + ylim(0,850) + geom_text(aes(x = wmean+.03, label = 'Observed', y = 525), color = 'red') + theme_classic()

# DabTram to Cis
# Find the weighted mean of the maximum percentage of cells in a single cluster per lineage in each simulation
wmean <- weighted.mean(unlist(lapply(dabtramtocis_lin_clust_list, function(x) max(x$Pcnt_cells))),
unlist(lapply(dabtramtocis_lin_clust_list, function(x) sum(x$Num_cells))))
df <- data.frame(weighted_mean = sapply(1:length(dabtramtocis_lin_clust_rand_list), function(y)
  weighted.mean(unlist(lapply(dabtramtocis_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells))),
                unlist(lapply(dabtramtocis_lin_clust_rand_list[[y]], function(x) sum(x$Num_cells))))))

ggplot(df, aes(x = weighted_mean))+geom_histogram(fill = '#9D85BE', color = 'black', binwidth = 0.005) + geom_vline(xintercept = wmean, linetype = 'dashed', color = 'red') + geom_text(aes(x = wmean+.03, label = round(wmean, 3), y = 500), color = 'red') + labs(y = 'Number of simulations', x = 'Weighted mean of percent of cells in dominant lineage', title = 'DabTram to Cis') + xlim(0.15, 0.65) + ylim(0,850) + geom_text(aes(x = wmean+.03, label = 'Observed', y = 525), color = 'red') + theme_classic()

# CoCl2
# Find the weighted mean of the maximum percentage of cells in a single cluster per lineage in each simulation
wmean <- weighted.mean(unlist(lapply(cocl2_lin_clust_list, function(x) max(x$Pcnt_cells))),
unlist(lapply(cocl2_lin_clust_list, function(x) sum(x$Num_cells))))
df <- data.frame(weighted_mean = sapply(1:length(cocl2_lin_clust_rand_list), function(y)
  weighted.mean(unlist(lapply(cocl2_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells))),
                unlist(lapply(cocl2_lin_clust_rand_list[[y]], function(x) sum(x$Num_cells))))))

ggplot(df, aes(x = weighted_mean))+geom_histogram(fill = '#0F8241', color = 'black', binwidth = 0.005) + geom_vline(xintercept = wmean, linetype = 'dashed', color = 'red') + geom_text(aes(x = wmean+.03, label = round(wmean, 3), y = 500), color = 'red') + labs(y = 'Number of simulations', x = 'Weighted mean of percent of cells in dominant lineage', title = 'CoCl2') + xlim(0.15, 0.65) + ylim(0,850) + geom_text(aes(x = wmean+.03, label = 'Observed', y = 525), color = 'red') + theme_classic()

# CoCl2 to DabTram
# Find the weighted mean of the maximum percentage of cells in a single cluster per lineage in each simulation
wmean <- weighted.mean(unlist(lapply(cocl2todabtram_lin_clust_list, function(x) max(x$Pcnt_cells))),
unlist(lapply(cocl2todabtram_lin_clust_list, function(x) sum(x$Num_cells))))
df <- data.frame(weighted_mean = sapply(1:length(cocl2todabtram_lin_clust_rand_list), function(y)
  weighted.mean(unlist(lapply(cocl2todabtram_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells))),
                unlist(lapply(cocl2todabtram_lin_clust_rand_list[[y]], function(x) sum(x$Num_cells))))))

ggplot(df, aes(x = weighted_mean))+geom_histogram(fill = '#10413B', color = 'black', binwidth = 0.005) + geom_vline(xintercept = wmean, linetype = 'dashed', color = 'red') + geom_text(aes(x = wmean+.03, label = round(wmean, 3), y = 500), color = 'red') + labs(y = 'Number of simulations', x = 'Weighted mean of percent of cells in dominant lineage', title = 'CoCl2 to DabTram') + xlim(0.15, 0.65) + ylim(0,850) + geom_text(aes(x = wmean+.03, label = 'Observed', y = 525), color = 'red') + theme_classic()

# CoCl2 to CoCl2
# Find the weighted mean of the maximum percentage of cells in a single cluster per lineage in each simulation
wmean <- weighted.mean(unlist(lapply(cocl2tococl2_lin_clust_list, function(x) max(x$Pcnt_cells))),
unlist(lapply(cocl2tococl2_lin_clust_list, function(x) sum(x$Num_cells))))
df <- data.frame(weighted_mean = sapply(1:length(cocl2tococl2_lin_clust_rand_list), function(y)
  weighted.mean(unlist(lapply(cocl2tococl2_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells))),
                unlist(lapply(cocl2tococl2_lin_clust_rand_list[[y]], function(x) sum(x$Num_cells))))))

ggplot(df, aes(x = weighted_mean))+geom_histogram(fill = '#6ABD45', color = 'black', binwidth = 0.005) + geom_vline(xintercept = wmean, linetype = 'dashed', color = 'red') + geom_text(aes(x = wmean+.03, label = round(wmean, 3), y = 500), color = 'red') + labs(y = 'Number of simulations', x = 'Weighted mean of percent of cells in dominant lineage', title = 'CoCl2 to CoCl2') + xlim(0.15, 0.65) + ylim(0,850) + geom_text(aes(x = wmean+.03, label = 'Observed', y = 525), color = 'red') + theme_classic()

# CoCl2 to Cis
# Find the weighted mean of the maximum percentage of cells in a single cluster per lineage in each simulation
wmean <- weighted.mean(unlist(lapply(cocl2tocis_lin_clust_list, function(x) max(x$Pcnt_cells))),
unlist(lapply(cocl2tocis_lin_clust_list, function(x) sum(x$Num_cells))))
df <- data.frame(weighted_mean = sapply(1:length(cocl2tocis_lin_clust_rand_list), function(y)
  weighted.mean(unlist(lapply(cocl2tocis_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells))),
                unlist(lapply(cocl2tocis_lin_clust_rand_list[[y]], function(x) sum(x$Num_cells))))))

ggplot(df, aes(x = weighted_mean))+geom_histogram(fill = '#6DC49C', color = 'black', binwidth = 0.005) + geom_vline(xintercept = wmean, linetype = 'dashed', color = 'red') + geom_text(aes(x = wmean+.03, label = round(wmean, 3), y = 500), color = 'red') + labs(y = 'Number of simulations', x = 'Weighted mean of percent of cells in dominant lineage', title = 'CoCl2 to Cis') + xlim(0.15, 0.65) + ylim(0,850) + geom_text(aes(x = wmean+.03, label = 'Observed', y = 525), color = 'red') + theme_classic()

# Cis
# Find the weighted mean of the maximum percentage of cells in a single cluster per lineage in each simulation
wmean <- weighted.mean(unlist(lapply(cis_lin_clust_list, function(x) max(x$Pcnt_cells))),
unlist(lapply(cis_lin_clust_list, function(x) sum(x$Num_cells))))
df <- data.frame(weighted_mean = sapply(1:length(cis_lin_clust_rand_list), function(y)
  weighted.mean(unlist(lapply(cis_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells))),
                unlist(lapply(cis_lin_clust_rand_list[[y]], function(x) sum(x$Num_cells))))))

ggplot(df, aes(x = weighted_mean))+geom_histogram(fill = '#C96D29', color = 'black', binwidth = 0.005) + geom_vline(xintercept = wmean, linetype = 'dashed', color = 'red') + geom_text(aes(x = wmean+.03, label = round(wmean, 3), y = 500), color = 'red') + labs(y = 'Number of simulations', x = 'Weighted mean of percent of cells in dominant lineage', title = 'Cis') + xlim(0.15, 0.65) + ylim(0,850) + geom_text(aes(x = wmean+.03, label = 'Observed', y = 525), color = 'red') + theme_classic()

# Cis to DabTram
# Find the weighted mean of the maximum percentage of cells in a single cluster per lineage in each simulation
wmean <- weighted.mean(unlist(lapply(cistodabtram_lin_clust_list, function(x) max(x$Pcnt_cells))),
unlist(lapply(cistodabtram_lin_clust_list, function(x) sum(x$Num_cells))))
df <- data.frame(weighted_mean = sapply(1:length(cistodabtram_lin_clust_rand_list), function(y)
  weighted.mean(unlist(lapply(cistodabtram_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells))),
                unlist(lapply(cistodabtram_lin_clust_rand_list[[y]], function(x) sum(x$Num_cells))))))

ggplot(df, aes(x = weighted_mean))+geom_histogram(fill = '#A23622', color = 'black', binwidth = 0.005) + geom_vline(xintercept = wmean, linetype = 'dashed', color = 'red') + geom_text(aes(x = wmean+.03, label = round(wmean, 3), y = 500), color = 'red') + labs(y = 'Number of simulations', x = 'Weighted mean of percent of cells in dominant lineage', title = 'Cis to DabTram') + xlim(0.15, 0.65) + ylim(0,850) + geom_text(aes(x = wmean+.03, label = 'Observed', y = 525), color = 'red') + theme_classic()

# Cis to CoCl2
# Find the weighted mean of the maximum percentage of cells in a single cluster per lineage in each simulation
wmean <- weighted.mean(unlist(lapply(cistococl2_lin_clust_list, function(x) max(x$Pcnt_cells))),
unlist(lapply(cistococl2_lin_clust_list, function(x) sum(x$Num_cells))))
df <- data.frame(weighted_mean = sapply(1:length(cistococl2_lin_clust_rand_list), function(y)
  weighted.mean(unlist(lapply(cistococl2_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells))),
                unlist(lapply(cistococl2_lin_clust_rand_list[[y]], function(x) sum(x$Num_cells))))))

ggplot(df, aes(x = weighted_mean))+geom_histogram(fill = '#C96D29', color = 'black', binwidth = 0.005) + geom_vline(xintercept = wmean, linetype = 'dashed', color = 'red') + geom_text(aes(x = wmean+.03, label = round(wmean, 3), y = 500), color = 'red') + labs(y = 'Number of simulations', x = 'Weighted mean of percent of cells in dominant lineage', title = 'Cis to CoCl2') + xlim(0.15, 0.65) + ylim(0,850) + geom_text(aes(x = wmean+.03, label = 'Observed', y = 525), color = 'red') + theme_classic()

# Cis to Cis
# Find the weighted mean of the maximum percentage of cells in a single cluster per lineage in each simulation
wmean <- weighted.mean(unlist(lapply(cistocis_lin_clust_list, function(x) max(x$Pcnt_cells))),
unlist(lapply(cistocis_lin_clust_list, function(x) sum(x$Num_cells))))
df <- data.frame(weighted_mean = sapply(1:length(cistocis_lin_clust_rand_list), function(y)
  weighted.mean(unlist(lapply(cistocis_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells))),
                unlist(lapply(cistocis_lin_clust_rand_list[[y]], function(x) sum(x$Num_cells))))))

ggplot(df, aes(x = weighted_mean))+geom_histogram(fill = '#FBD08C', color = 'black', binwidth = 0.005) + geom_vline(xintercept = wmean, linetype = 'dashed', color = 'red') + geom_text(aes(x = wmean+.03, label = round(wmean, 3), y = 500), color = 'red') + labs(y = 'Number of simulations', x = 'Weighted mean of percent of cells in dominant lineage', title = 'Cis to Cis') + xlim(0.15, 0.65) + ylim(0,850) + geom_text(aes(x = wmean+.03, label = 'Observed', y = 525), color = 'red') + theme_classic()
dev.off()

```



